Systems and methods for reconstructing cardiac images

ABSTRACT

A method for reconstructing target cardiac images is provided. The method may include: obtaining a plurality of projection data corresponding to a plurality of cardiac motion phases; determining a plurality of cardiac motion parameters corresponding to at least a portion of the plurality of cardiac motion phases based on the plurality of projection data; determining a phase of interest based on the plurality of cardiac motion parameters; and/or reconstructing the one or more target cardiac images of the phase of interest

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.17/202,308, filed on Mar. 15, 2021, which is a continuation of U.S.application Ser. No. 16/437,003 (issued as U.S. Pat. No. 10,950,016),filed on Jun. 11, 2019, which claims priority to Chinese PatentApplication No. 201811133622.6, filed on Sep. 27, 2018, Chinese PatentApplication No. 201811133609.0, filed on Sep. 27, 2018, Chinese PatentApplication No. 201810597965.1, filed on Jun. 11, 2018, Chinese PatentApplication No. 201811134373.2, filed on Sep. 27, 2018, and ChinesePatent Application No. 201811134375.1, filed on Sep. 27, 2018, thecontents of each of which are hereby incorporated by reference in itsentirety.

TECHNICAL FIELD

The present disclosure generally relates to imaging technology, and morespecifically relates to systems and methods for reconstructing cardiacimages.

BACKGROUND

Cardiac image reconstruction is a routine process for clinical computedtomography (CT) application. The clarity of coronary angiography iscritical to the quality of reconstructed cardiac images. Due to thephysiological characteristics of the heart (especially the rightcoronary artery), motion artifacts related to heart beating are easilygenerated during scanning, resulting in a blurred coronary artery inimages and low image quality, which can affect diagnosis.

In order to reduce artifacts, data of the diastolic period (75% phase)are used to reconstruct the cardiac images. In theory, the motion rateof the heart in the diastolic period is slower than that of otherphases, and thus, the impact of data inconsistency may be attenuated toa certain degree. However, a reconstruction based on data of 75% phaseis not suitable for all patients. The patients' heart rates aregenerally different, and respiratory movement can induce an impact, sothat doctors may need to reconstruct a number (or count) of images ofdifferent phases offline, and determine an appropriate phase for furtherdiagnosis through tedious comparisons.

With the development of medical device and technology, the use of CTscanners for detecting lesions of patients becomes mature. When using aCT scanner, the CT scanner collects data of multiple phases, and imagereconstruction can be performed based on the data of multiple phases toobtain an image of a patient's lesion. It is necessary to use data of anoptimal phase for image reconstruction due to the movement of the heartwhen the heart is scanned using the CT scanner.

In some situations, image reconstruction is performed for each of themultiple phases to obtain multiple reconstructed images corresponding tothe multiple phases according to the data of the multiple phases, and animage of an optimal phase is obtained by comparing the multiplereconstructed images. A heart region may need to be identified in thereconstructed image of the optimal phase to obtain a cardiac image. Inthis way, the image reconstruction operation is generally performed onthe data of all the multiple phases. Accordingly, the operation for alarge amount of data can consume a large amount of time, and causeproblems such as low operational efficiency.

Therefore, it is desirable to provide systems and methods fordetermining an optimal phase automatically; providing a relatively smallfield of view for reconstruction; reconstructing cardiac images thathave relatively high image qualities and are affected by cardiac motionto a minimum extent, efficiently, cost-effectively, and without waste oftime and/or resources.

SUMMARY

In one aspect of the present disclosure, a method for reconstructing oneor more target cardiac images is provided. The method may include one ormore of the following operations: obtaining a plurality of projectiondata corresponding to a plurality of cardiac motion phases;reconstructing, in an initial field of view (FOV), at least one previewimage based on at least a portion of the plurality of projection data;obtaining a thoracic contour image based on the at least one previewimage; determining one or more positions of a thoracic contour boundaryin the thoracic contour image; determining a reconstruction center basedon the one or more positions of the thoracic contour boundary; andreconstructing, according to the reconstruction center, the one or moretarget cardiac images based on at least a portion of the plurality ofprojection data.

In another aspect of the present disclosure, a system for reconstructingone or more target cardiac images is provided. The system may include atleast one storage device storing a set of instructions; and at least oneprocessor in communication with the storage device, wherein whenexecuting the set of instructions, the at least one processor isconfigured to cause the system to perform operations of the methodmentioned above.

In another aspect of the present disclosure, a non-transitory computerreadable medium storing instructions is provided. The instructions, whenexecuted by at least one processor, may cause the at least one processorto implement the method mentioned above.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary imaging systemaccording to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating exemplary hardware andsoftware components of a computing device according to some embodimentsof the present disclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device that is configured toimplement a specific system disclosed in the present disclosure;

FIG. 4 is a flowchart illustrating an exemplary process forreconstructing one or more target cardiac images according to someembodiments of the present disclosure;

FIG. 5A is a flowchart illustrating an exemplary process forreconstructing cardiac images according to some embodiments of thepresent disclosure;

FIG. 5B is a schematic diagram illustrating an exemplary ECG signal andcorresponding phases according to some embodiments of the presentdisclosure;

FIG. 6 is a flowchart illustrating an exemplary process for determininga mean phase according to some embodiments of the present disclosure;

FIG. 7 is a flowchart illustrating another exemplary process fordetermining a mean phase according to some embodiments of the presentdisclosure;

FIG. 8 is a flowchart illustrating an exemplary process for extractingvascular image(s) of interest according to some embodiments of thepresent disclosure;

FIG. 9 is a flowchart illustrating an exemplary process for obtainingventricular image(s) according to some embodiments of the presentdisclosure;

FIG. 10 is a flowchart illustrating an exemplary process for extractingvascular image(s) of interest according to some embodiments of thepresent disclosure;

FIG. 11 is a flowchart illustrating an exemplary process for determiningcardiac motion parameter(s) according to some embodiments of the presentdisclosure;

FIG. 12 is a flowchart illustrating an exemplary process for determininga mean phase based on motion rate(s) of vascular center(s) according tosome embodiments of the present disclosure;

FIG. 13 is a flowchart illustrating an exemplary process for obtaining aset of target cardiac images according to some embodiments of thepresent disclosure;

FIG. 14 is a flowchart illustrating an exemplary process for determininga phase of interest in a cardiac cycle according to some embodiments ofthe present disclosure;

FIG. 15 is a flowchart illustrating an exemplary process for determiningsegment threshold(s) according to some embodiments of the presentdisclosure;

FIG. 16 is a flowchart illustrating an exemplary process for determiningregularity degree(s) according to some embodiments of the presentdisclosure;

FIG. 17 is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure;

FIG. 18 is a flowchart illustrating an exemplary process forreconstructing cardiac image(s) according to some embodiments of thepresent disclosure;

FIG. 19 is a flowchart illustrating an exemplary process for determininga thoracic contour image according to some embodiments of the presentdisclosure;

FIG. 20 is a flowchart illustrating an exemplary process for determiningone or more positions of a thoracic contour boundary according to someembodiments of the present disclosure;

FIG. 21 is a flowchart illustrating an exemplary process forreconstructing cardiac image(s) according to some embodiments of thepresent disclosure;

FIG. 22 is an exemplary maximum intensity projection image relating tobone(s) and a contrast agent according to some embodiments of thepresent disclosure;

FIG. 23 is an exemplary maximum intensity projection image relating to acontrast agent according to some embodiments of the present disclosure;

FIG. 24 is an exemplary thoracic contour image according to someembodiments of the present disclosure;

FIG. 25 is an exemplary left thoracic contour image according to someembodiments of the present disclosure;

FIG. 26 is an exemplary right thoracic contour image according to someembodiments of the present disclosure;

FIG. 27 is an image illustrating a region to be analyzed according tosome embodiments of the present disclosure;

FIG. 28 is a block diagram illustrating an exemplary processing devicefor cardiac image reconstruction according to some embodiments of thepresent disclosure;

FIG. 29 is a block diagram illustrating an exemplary maximum intensityprojection module according to some embodiments of the presentdisclosure;

FIG. 30 is a block diagram illustrating an exemplary maximum intensityprojection unit according to some embodiments of the present disclosure;

FIG. 31 is a block diagram illustrating an exemplary boundarydetermination module according to some embodiments of the presentdisclosure;

FIG. 32 is a block diagram illustrating an exemplary left boundarydetermination unit according to some embodiments of the presentdisclosure;

FIG. 33 is a block diagram illustrating an exemplary right boundarydetermination unit according to some embodiments of the presentdisclosure;

FIG. 34 is a block diagram illustrating an exemplary upper boundarydetermination unit according to some embodiments of the presentdisclosure; and

FIG. 35 is a schematic diagram illustrating an exemplary computingdevice according to some embodiments of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well-known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof. It will be understood that the term “object” and“subject” may be used interchangeably as a reference to a thing thatundergoes a treatment and/or an imaging procedure in a radiation systemof the present disclosure.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, section or assembly of differentlevel in ascending order. However, the terms may be displaced by anotherexpression if they achieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or another storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or themselves,and/or may be invoked in response to detected events or interrupts.Software modules/units/blocks configured for execution on computingdevices (e.g., processor 210 as illustrated in FIG. 2 ) may be providedon a computer-readable medium, such as a compact disc, a digital videodisc, a flash drive, a magnetic disc, or any other tangible medium, oras a digital download (and can be originally stored in a compressed orinstallable format that needs installation, decompression, or decryptionprior to execution). Such software code may be stored, partially orfully, on a storage device of the executing computing device, forexecution by the computing device. Software instructions may be embeddedin firmware, such as an EPROM. It will be further appreciated thathardware modules/units/blocks may be included in connected logiccomponents, such as gates and flip-flops, and/or can be included ofprogrammable units, such as programmable gate arrays or processors. Themodules/units/blocks or computing device functionality described hereinmay be implemented as software modules/units/blocks but may berepresented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description mayapply to a system, an engine, or a portion thereof.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, engine, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

The flowcharts used in the present disclosure illustrate operations thatsystems implement according to some embodiments of the presentdisclosure. It is to be expressly understood, the operations of theflowcharts may be implemented not in order. Conversely, the operationsmay be implemented in inverted order, or simultaneously. Moreover, oneor more other operations may be added to the flowcharts. One or moreoperations may be removed from the flowcharts.

One aspect of the present disclosure relates to methods, systems,computing devices, and computer readable storage mediums for cardiacimage reconstruction, which may select a plurality of sampled data atregular intervals in one or more cardiac cycles, obtain correspondingreconstructed image(s), and determine a mean phase according to thereconstructed image(s) of a plurality of phases. Image datacorresponding to phases in a preset range near the mean phase may beselected in each cardiac cycle to determine a phase of interest in theeach cardiac cycle, and accordingly, a sequence of cardiac images ofphases of interest may be obtained. The methods can accurately determinean optimal phase of each cardiac cycle, reduce artifacts induced bycardiac motion, and improve the image quality of the reconstructedcardiac image(s).

Another aspect of the present disclosure relates to methods, systems,computing devices, and computer readable storage mediums for cardiacimage reconstruction, which may obtain a plurality of preview images,obtain a thoracic contour image based on the plurality of preview imagesand a maximum intensity projection algorithm, and then determine aleftmost boundary position, a rightmost boundary position and anuppermost boundary position based on the thoracic contour image, andfurther determine a reconstruction center. A multi-phase reconstructionmay be performed according to the reconstruction center and a presetfield of view for reconstruction, to obtain the cardiac images. Bydetermining the heart region first and performing multi-phasereconstruction according to the image data of heart region, the amountof data used for reconstruction and the time for importing the data canbe reduced, and the operation efficiency can be improved.

According to a further aspect of the present disclosure, in cardiacimage reconstruction, a phase of interest may be determined for aspecific cardiac cycle, and accordingly, the reconstructed cardiacimage(s) of the phase of interest may have relatively low artifact(s)and high image quality. According to a still further aspect of thepresent disclosure, a relatively small field of view (FOV) may be usedin cardiac image reconstruction, and accordingly, the operationefficiency may be improved. Specifically, the relatively small FOV maybe used in the reconstruction of the cardiac image(s) for thedetermination of the mean phase or phase of interest, or the relativelysmall FOV may be used in the reconstruction of cardiac image(s) ofinterest after the mean phase or phase of interest is determined. Thesystem and method may reduce the artifacts induced by cardiac motion,reduce the amount of data used for reconstruction and the time forimporting the data, improve the image quality of the reconstructedcardiac images, and improve the operation efficiency.

In order to make the objects, technical solutions and advantages of thepresent disclosure more clear, the present disclosure will be furtherdescribed in detail below with reference to the accompanying drawingsand embodiments. It is understood that the specific embodimentsdescribed herein are merely illustrative of the present disclosure andare not intended to limit the present disclosure.

A computed tomography (CT) device may include a gantry, a scanning bed,and a console for the physician to operate. A tube may be disposed onone side of the gantry, and detectors may be disposed on a side oppositeto the tube. The console may include a computing device that controls CTscanning. The computing device may be also used to receive scan datacollected by the detectors, process the scan data and reconstruct CTimage(s). When scanning with CT, a patient may lie on the scanning bed,and the patient may be translated into the aperture of the gantry by thescanning bed. The tube disposed on the gantry may emit X-rays, and theX-rays may be received by the detectors to generate scan data. The scandata may be transmitted to the computing device, and the computingdevice may perform preliminary processing on the scan data and imagereconstruction to obtain CT image(s).

It should be noted that a relative position, e.g., left, right, upper,lower or underneath, or the like in the present disclosure may refer tothe relative positions in the image(s). For example, an upper positionin an image may be closer to the upper boundary of the image than thelower position; a lower position in the image may be closer to the lowerboundary of the image than the upper position. A left position in animage may be closer to the left boundary of the image than the rightposition; a right position in an image may be closer to the rightboundary of the image than the left position. Furthermore, the sagittalaxis (also referred to as the Y axis) may refer to the horizontal linein the anterior to posterior direction, the coronal (frontal) axis (alsoreferred to as the X axis) may refer to the horizontal line in the left(of the object) to right (of the object) direction, and the verticalaxis (also referred to as the Z axis) may refer to the perpendicularline in the superior to inferior direction, which is perpendicular tothe horizontal line. And the sagittal plane may refer to the tangentplane along with the sagittal axis and vertical axis, which may segmentthe object into left and right sections; the coronal (frontal) plane mayrefer to the tangent plane along with the coronal (frontal) axis andvertical axis, which may segment the object into anterior and posteriorsections; and the transverse plane may refer to the tangent plane alongwith the sagittal axis and coronal (frontal) axis, which may segment theobject into superior and inferior sections.

FIG. 1 is a schematic diagram illustrating an exemplary imaging systemaccording to some embodiments of the present disclosure. As shown inFIG. 1 , the imaging system 100 may include a scanner 110, a network120, one or more terminals 130, a processing device 140, and a storagedevice 150. The components in the imaging system 100 may be connected inone or more of various ways. Merely by way of example, the scanner 110may be connected to the processing device 140 through the network 120.As another example, the scanner 110 may be connected to the processingdevice 140 directly as indicated by the bi-directional arrow in dottedlines linking the scanner 110 and the processing device 140. As stillanother example, the storage device 150 may be connected to theprocessing device 140 directly or through the network 120. As stillanother example, the terminal 130 may be connected to the processingdevice 140 directly (as indicated by the bi-directional arrow in dottedlines linking the terminal 130 and the processing device 140) or throughthe network 120.

The scanner 110 may scan an object and/or generate scan data relating tothe object. In some embodiments, the scanner 110 may be asingle-modality medical imaging device (e.g., a magnetic resonanceimaging (MRI) device, a positron emission tomography (PET) device, asingle-photon emission computed tomography (SPECT) device, a computedtomography (CT) device, or the like) or a multi-modality medical imagingdevice (e.g., a PET-MRI device, a SPECT-MRI device, or a PET-CT device).In some embodiments, the scanner 110 may include a gantry configured toimaging the object, a detection region configure to accommodate theobject, and/or a scanning bed configured to support the object during animaging process. The scanning bed may support the object duringscanning. For example, the object may be supported and/or delivered tothe detection region of the gantry by the scanning bed. In someembodiments, the scanner 110 may transmit image(s) via the network 120to the processing device 140, the storage device 150, and/or theterminal(s) 130. For example, the image(s) may be sent to the processingdevice 140 for further processing or may be stored in the storage device150.

In some embodiments, the object may be biological or non-biological.Merely by way of example, the object may include a patient, an organ, atissue, a specimen, a man-made object, a phantom, etc. In someembodiments, the object to be scanned (also referred to as imaged) mayinclude a body, substance, or the like, or any combination thereof. Insome embodiments, the object may include a specific portion of a body,such as a head, a thorax, an abdomen, or the like, or any combinationthereof. In some embodiments, the object may include a specific organ,such as a breast, an esophagus, a trachea, a bronchus, a stomach, agallbladder, a small intestine, a colon, a bladder, a ureter, a uterus,a fallopian tube, etc. In the present disclosure, “object” and “subject”are used interchangeably.

The network 120 may include any suitable network that can facilitate theexchange of information and/or data for the imaging system 100. In someembodiments, one or more components of the imaging system 100 (e.g., thescanner 110, the terminal 130, the processing device 140, the storagedevice 150, etc.) may communicate information and/or data with one ormore other components of the imaging system 100 via the network 120. Forexample, the processing device 140 may obtain image data from thescanner 110 via the network 120. As another example, the processingdevice 140 may obtain user instructions from the terminal 130 via thenetwork 120. The network 120 may be and/or include a public network(e.g., the Internet), a private network (e.g., a local area network(LAN), a wide area network (WAN)), etc.), a wired network (e.g., anEthernet network), a wireless network (e.g., an 802.11 network, a Wi-Finetwork, etc.), a cellular network (e.g., a Long Term Evolution (LTE)network), a frame relay network, a virtual private network (“VPN”), asatellite network, a telephone network, routers, hubs, switches, servercomputers, and/or any combination thereof. Merely by way of example, thenetwork 120 may include a cable network, a wireline network, afiber-optic network, a telecommunications network, an intranet, awireless local area network (WLAN), a metropolitan area network (MAN), apublic telephone switched network (PSTN), a Bluetooth™ network, aZigBee™ network, a near field communication (NFC) network, or the like,or any combination thereof. In some embodiments, the network 120 mayinclude one or more network access points. For example, the network 120may include wired and/or wireless network access points such as basestations and/or internet exchange points through which one or morecomponents of the imaging system 100 may be connected to the network 120to exchange data and/or information.

The terminal(s) 130 may include a mobile device 131, a tablet computer132, a laptop computer 133, or the like, or any combination thereof. Insome embodiments, the mobile device 131 may include a smart home device,a wearable device, a mobile device, a virtual reality device, anaugmented reality device, or the like, or any combination thereof. Insome embodiments, the smart home device may include a smart lightingdevice, a control device of an intelligent electrical apparatus, a smartmonitoring device, a smart television, a smart video camera, aninterphone, or the like, or any combination thereof. In someembodiments, the wearable device may include a bracelet, a footgear,eyeglasses, a helmet, a watch, clothing, a backpack, a smart accessory,or the like, or any combination thereof. In some embodiments, the mobiledevice may include a mobile phone, a personal digital assistant (PDA), agaming device, a navigation device, a point of sale (POS) device, alaptop, a tablet computer, a desktop, or the like, or any combinationthereof. In some embodiments, the virtual reality device and/or theaugmented reality device may include a virtual reality helmet, virtualreality glasses, a virtual reality patch, an augmented reality helmet,augmented reality glasses, an augmented reality patch, or the like, orany combination thereof. For example, the virtual reality device and/orthe augmented reality device may include a Google Glass™, an OculusRift™, a Hololens™, a Gear VR™, etc. In some embodiments, theterminal(s) 130 may be part of the processing device 140.

The processing device 140 may process data and/or information obtainedfrom the scanner 110, the terminal 130, and/or the storage device 150.In some embodiments, the processing device 140 may be a single server ora server group. The server group may be centralized or distributed. Insome embodiments, the processing device 140 may be local or remote. Forexample, the processing device 140 may access information and/or datastored in the scanner 110, the terminal 130, and/or the storage device150 via the network 120. As another example, the processing device 140may be directly connected to the scanner 110, the terminal 130 and/orthe storage device 150 to access stored information and/or data. As afurther example, the processing device 140 may process the data obtainedfrom the scanner 110, and reconstruct cardiac images. In someembodiments, the processing device 140 may be implemented on a cloudplatform. Merely by way of example, the cloud platform may include aprivate cloud, a public cloud, a hybrid cloud, a community cloud, adistributed cloud, an inter-cloud, a multi-cloud, or the like, or anycombination thereof. In some embodiments, the processing device 140 maybe implemented by a computing device 200 having one or more componentsas illustrated in FIG. 2 . In some embodiments, the processing device140, or a portion of the processing device 140 may be integrated intothe scanner 110.

The storage device 150 may store data, instructions, and/or any otherinformation. In some embodiments, the storage device 150 may store dataobtained from the terminal 130 and/or the processing device 140. In someembodiments, the storage device 150 may store data and/or instructionsthat the processing device 140 may execute or use to perform exemplarymethods described in the present disclosure. In some embodiments, thestorage device 150 may include mass storage, removable storage, avolatile read-and-write memory, a read-only memory (ROM), or the like,or any combination thereof. Exemplary mass storage devices may include amagnetic disk, an optical disk, a solid-state drive, etc. Exemplaryremovable storage devices may include a flash drive, a floppy disk, anoptical disk, a memory card, a zip disk, a magnetic tape, etc. Exemplaryvolatile read-and-write memories may include a random access memory(RAM). Exemplary RAM may include a dynamic RAM (DRAM), a double daterate synchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), athyristor RAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc. ExemplaryROM may include a mask ROM (MROM), a programmable ROM (PROM), anerasable programmable ROM (EPROM), an electrically erasable programmableROM (EEPROM), a compact disk ROM (CD-ROM), and a digital versatile diskROM, etc. In some embodiments, the storage device 150 may be implementedon a cloud platform. Merely by way of example, the cloud platform mayinclude a private cloud, a public cloud, a hybrid cloud, a communitycloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like,or any combination thereof.

In some embodiments, the storage device 150 may be connected to thenetwork 120 to communicate with one or more other components in theimaging system 100 (e.g., the processing device 140, the terminal 130,etc.). One or more components of the imaging system 100 may access thedata or instructions stored in the storage device 150 via the network120. In some embodiments, the storage device 150 may be directlyconnected to or communicate with one or more other components of theimaging system 100 (e.g., the processing device 140, the terminal 130,etc.). In some embodiments, the storage device 150 may be part of theprocessing device 140.

FIG. 2 is a schematic diagram illustrating exemplary hardware andsoftware components of a computing device according to some embodimentsof the present disclosure. The computing device 200 may be a generalpurpose computer or a special purpose computer; both may be used toimplement an imaging system 100 of the present disclosure. In someembodiments, the processing device 140 may be implemented on thecomputing device 200, via its hardware, software program, firmware, or acombination thereof. Although only one such computer is shown, forconvenience, the computer functions as described herein may beimplemented in a distributed manner on a number of similar platforms, todistribute the processing load. As illustrated in FIG. 2 , the computingdevice 200 may include a processor 210, a storage 220, an input/output(I/O) 230, and a communication port 240.

The processor 210 may execute computer instructions (e.g., program code)and perform functions of the processor in accordance with techniquesdescribed herein. The computer instructions may include, for example,routines, programs, objects, components, data structures, procedures,modules, and functions, which perform particular functions describedherein. For example, the processor 210 may obtain a plurality ofprojection data generated by an imaging device (e.g., the scanner 110).In some embodiments, the processor 210 may reconstruct, in an initialfield of view (FOV), at least one preview image based on at least aportion of the plurality of projection data. In some embodiments, theprocessor 210 may determine a reconstruction center based on one or morepositions of a thoracic contour boundary associated with the at leastone preview image. In some embodiments, the processor 210 may select aplurality of cardiac motion phases. In some embodiments, the processor210 may reconstruct, in a preset FOV smaller than the initial FOV and atthe reconstruction center, one or more cardiac images of the eachselected cardiac motion phase based on the one or more sub-sets ofprojection data corresponding to the each selected cardiac motion phase.In some embodiments, the processor 210 may determine a phase of interestbased on a plurality of cardiac motion parameters corresponding to theplurality of selected cardiac motion phases. In some embodiments, theprocessor 210 may reconstruct a target cardiac image of the phase ofinterest.

In some embodiments, the processor 210 may include one or more hardwareprocessors, such as a microcontroller, a microprocessor, a reducedinstruction set computer (RISC), an application specific integratedcircuits (ASICs), an application-specific instruction-set processor(ASIP), a central processing unit (CPU), a graphics processing unit(GPU), a physics processing unit (PPU), a microcontroller unit, adigital signal processor (DSP), a field programmable gate array (FPGA),an advanced RISC machine (ARM), a programmable logic device (PLD), anycircuit or processor capable of executing one or more functions, or thelike, or any combinations thereof.

Merely for illustration, only one processor is described in thecomputing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multipleprocessors, thus operations and/or method steps that are performed byone processor as described in the present disclosure may also be jointlyor separately performed by the multiple processors. For example, if inthe present disclosure the processor of the computing device 200executes both operation A and operation B, it should be understood thatoperation A and operation B may also be performed by two or moredifferent processors jointly or separately in the computing device 200(e.g., a first processor executes operation A and a second processorexecutes operation B, or the first and second processors jointly executeoperations A and B).

The storage 220 may store data/information obtained from the scanner110, the terminal 130, the storage device 150, and/or any othercomponent of the imaging system 100. In some embodiments, the storage220 may include a mass storage device, a removable storage device, avolatile read-and-write memory, a read-only memory (ROM), or the like,or any combination thereof. For example, the mass storage may include amagnetic disk, an optical disk, a solid-state drive, etc. The removablestorage may include a flash drive, a floppy disk, an optical disk, amemory card, a zip disk, a magnetic tape, etc. The volatileread-and-write memory may include a random access memory (RAM). The RAMmay include a dynamic RAM (DRAM), a double date rate synchronous dynamicRAM (DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. The ROM may include a mask ROM (MROM),a programmable ROM (PROM), an erasable programmable ROM (EPROM), anelectrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the storage 220 may store one or more programs and/or instructions toperform exemplary methods described in the present disclosure. Forexample, the storage 220 may store a program for scanning the heart ofthe object and/or a program for reconstructing cardiac images.

The I/O 230 may input and/or output signals, data, information, etc. Insome embodiments, the I/O 230 may enable a user interaction with theprocessing device 140. In some embodiments, the I/O 230 may include aninput device and an output device. Examples of the input device mayinclude a keyboard, a mouse, a touch screen, a microphone, or the like,or a combination thereof. Examples of the output device may include adisplay device, a loudspeaker, a printer, a projector, or the like, or acombination thereof. Examples of the display device may include a liquidcrystal display (LCD), a light-emitting diode (LED)-based display, aflat panel display, a curved screen, a television device, a cathode raytube (CRT), a touch screen, or the like, or a combination thereof.

The communication port 240 may be connected to a network (e.g., thenetwork 120) to facilitate data communications. The communication port240 may establish connections between the processing device 140 and thescanner 110, the terminal 130, and/or the storage device 150. Theconnection may be a wired connection, a wireless connection, any othercommunication connection that can enable data transmission and/orreception, and/or any combination of these connections. The wiredconnection may include, for example, an electrical cable, an opticalcable, a telephone wire, or the like, or any combination thereof. Thewireless connection may include, for example, a Bluetooth™ link, aWi-Fi™ link, a WiMax™ link, a WLAN link, a ZigBee link, a mobile networklink (e.g., 3G, 4G, 5G, etc.), or the like, or a combination thereof. Insome embodiments, the communication port 240 may be and/or include astandardized communication port, such as RS232, RS485, etc. In someembodiments, the communication port 240 may be a specially designedcommunication port. For example, the communication port 240 may bedesigned in accordance with the digital imaging and communications inmedicine (DICOM) protocol.

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device that is configured toimplement a specific system disclosed in the present disclosure. Asillustrated in FIG. 3 , the mobile device 300 may include acommunication unit 310, a display 320, a graphics processing unit (GPU)330, a CPU 340, an I/O 350, a storage 390, and a memory 360. In someembodiments, any other suitable component, including but not limited toa system bus or a controller (not shown), may also be included in themobile device 300. In some embodiments, a mobile operating system 370(e.g., IOS™, Android™, Windows Phone™, etc.) and one or moreapplications 380 may be loaded into the memory 360 from the storage 390in order to be executed by the CPU 340. The applications 380 may includea browser or any other suitable mobile apps for receiving and renderinginformation relating to image processing or other information from theprocessing device 140. User interactions with the information stream maybe achieved via the I/O 350 and provided to the processing device 140and/or other components of the imaging system 100 via the network 120.In some embodiments, a user may input parameters to the imaging system100, via the mobile device 300.

In order to implement various modules, units and their functionsdescribed above, a computer hardware platform may be used as hardwareplatforms of one or more elements (e.g., the processing device 140and/or other components of the imaging system 100 described in FIG. 1 ).Since these hardware elements, operating systems and program languagesare common; it may be assumed that persons skilled in the art may befamiliar with these techniques and they may be able to provideinformation needed in the imaging according to the techniques describedin the present disclosure. A computer with the user interface may beused as a personal computer (PC), or other types of workstations orterminal devices. After being properly programmed, a computer with theuser interface may be used as a server. It may be considered that thoseskilled in the art may also be familiar with such structures, programs,or general operations of this type of computing device.

FIG. 4 is a flowchart illustrating an exemplary process forreconstructing one or more target cardiac images according to someembodiments of the present disclosure. Each image of the one or moretarget cardiac images may include a plurality of elements, each elementof the plurality of elements may be a pixel or voxel. In someembodiments, the one or more target cardiac images may correspond tovolume data of the heart of an object.

In some embodiments, the process 400 may be executed by the imagingsystem 100. For example, the process 400 may be implemented as a set ofinstructions (e.g., an application) stored in one or more storagedevices (e.g., the storage device 150, the storage 220, and/or thestorage 390) and invoked and/or executed by the processing device 140(implemented on, for example, the processor 210 of the computing device200, and the CPU 340 of the mobile device 300). The operations of theprocess 400 presented below are intended to be illustrative. In someembodiments, the process may be accomplished with one or more additionaloperations not described, and/or without one or more of the operationsdiscussed. Additionally, the order in which the operations of theprocess 400 as illustrated in FIG. 4 and described below is not intendedto be limiting.

In 402, a plurality of projection data may be obtained. The projectiondata may be generated by an imaging device. The processing device 140(e.g., the first reconstruction module 17200) may perform operation 402.The projection data may be obtained from the scanner 110, the storagedevice 150, an external data source, etc. More descriptions of theprojection data may be found elsewhere in the present disclosure (e.g.,FIG. 5 and descriptions thereof).

In 404, at least one preview image may be reconstructed, in an initialfield of view (FOV), based on a portion of the plurality of projectiondata. The processing device 140 (e.g., the first reconstruction module17200) may perform operation 404. The initial FOV may be a relativelylarge FOV (e.g., an FOV with a diameter of at least 500 mm). In someembodiments, the initial FOV may be set according to a default settingof the imaging system 100 or preset by a user or operator via theterminal(s) 130. The preview image(s) may be reconstructed based on oneor more reconstruction algorithms illustrated elsewhere in the presentdisclosure. More descriptions of the preview image(s) may be foundelsewhere in the present disclosure (e.g., FIGS. 18, 19 and 21 anddescriptions thereof).

In 406, a reconstruction center may be determined based on one or morepositions of a thoracic contour boundary associated with the at leastone preview image. The processing device 140 (e.g., the centerreconstruction module 28300) may perform operation 406. Moredescriptions of the determination of the position(s) of the thoraciccontour boundary and the determination of the reconstruction center maybe found elsewhere in the present disclosure (e.g., FIGS. 18, 20 and 21and descriptions thereof).

In 408, a plurality of cardiac motion phases may be selected. Theprocessing device 140 (e.g., the phase selection module 17100) mayperform operation 408. More descriptions of the selection of the cardiacmotion phase(s) may be found elsewhere in the present disclosure (e.g.,FIG. 5 and descriptions thereof).

In 410, one or more cardiac images of a (e.g., each) selected cardiacmotion phase (or sampled cardiac motion phase illustrated elsewhere inthe present disclosure) may be reconstructed, in a preset FOV smallerthan the initial FOV and/or at the reconstruction center, based on oneor more sub-sets of projection data corresponding to the selectedcardiac motion phase. The processing device 140 (e.g., the imagereconstruction module 28400) may perform operation 410. Thereconstruction of the cardiac image(s) of the selected cardiac motionphase(s) may be similar to the reconstruction of the target cardiacimage(s) illustrated in operations 18108 (see FIG. 18 ) and 21418 (seeFIG. 21 ). More descriptions of the preset FOV may be found elsewhere inthe present disclosure (e.g., FIGS. 18 and 21 and descriptions thereof).More descriptions of the image reconstruction in the preset FOV smallerthan the initial FOV and/or at the reconstruction center may be foundelsewhere in the present disclosure (e.g., FIG. 21 and descriptionsthereof).

In 412, a phase of interest may be determined based on a plurality ofcardiac motion parameters corresponding to the plurality of cardiacmotion phases. The processing device 140 (e.g., the secondreconstruction module 17500) may perform operation 412. Moredescriptions of the determination of the phase of interest may be foundelsewhere in the present disclosure (e.g., FIGS. 5 and 13 anddescriptions thereof).

In 414, a target cardiac image of the phase of interest may bereconstructed or determined. The processing device 140 (e.g., the secondreconstruction module 17500) may perform operation 414. Moredescriptions of the reconstruction or determination of the targetcardiac image of the phase of interest may be found elsewhere in thepresent disclosure (e.g., FIG. 13 and descriptions thereof).

According to the process 400, in cardiac image reconstruction, a phaseof interest may be determined for a specific cardiac cycle, andaccordingly, the reconstructed cardiac image(s) of the phase of interestmay have relatively low artifact(s) and high image quality. Besides, arelatively small field of view (FOV) may be used in cardiac imagereconstruction, and accordingly, the operation efficiency may beimproved. It should be noted that the relatively small FOV may be usedin the reconstruction of the cardiac image(s) for the determination ofthe mean phase or phase of interest, or the relatively small FOV may beused in the reconstruction of cardiac image(s) of interest after themean phase or phase of interest is determined. That is, in someembodiments, cardiac images of sampled cardiac motion phase(s) may bereconstructed based on the initial FOV, the phase of interest (or meanphase alternatively) may be determined based on the cardiac motionparameters of the cardiac images, and then the target cardiac image ofthe phase of interest (or mean phase alternatively) may be determinedbased on the preset FOV and/or the reconstruction center. The operationsmay reduce the artifacts induced by cardiac motion, reduce the amount ofdata used for reconstruction and the time for importing the data,improve the image quality of the reconstructed cardiac images, andimprove the operation efficiency.

It should be noted that the description of the following processes ismerely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. For example, one or more of the operations in FIG. 5 (or anyone of FIGS. 6-16 ) may be performed based on images reconstructed basedon the initial FOV. As another example, one or more of the operations inFIG. 5 (or any one of FIGS. 6-16 ) may be performed based on imagesreconstructed based on the preset FOV and/or reconstruction center.

FIG. 5A is a flowchart illustrating an exemplary process forreconstructing cardiac images according to some embodiments of thepresent disclosure. In some embodiments, the process 500 may be executedby the imaging system 100. For example, the process 500 may beimplemented as a set of instructions (e.g., an application) stored inone or more storage devices (e.g., the storage device 150, the storage220, and/or the storage 390) and invoked and/or executed by theprocessing device 140 (implemented on, for example, the processor 210 ofthe computing device 200, and the CPU 340 of the mobile device 300). Theoperations of the process 500 presented below are intended to beillustrative. In some embodiments, the process may be accomplished withone or more additional operations not described, and/or without one ormore of the operations discussed. Additionally, the order in which theoperations of the process 500 as illustrated in FIG. 5 and describedbelow is not intended to be limiting.

In an embodiment, as shown in FIG. 5 , an exemplary cardiac imagereconstruction process is provided. The process 500 may include thefollowing operations:

In 5102, a plurality of sampled cardiac motion phases may be obtained(e.g., at regular intervals).

In some embodiments, the processing device 140 (e.g., the phaseselection module 17100) may perform operation 5102. In some embodiments,cardiac motion phases may be denoted by phase angles (e.g., phase anglesfrom 0° to 360°). In some embodiments, cardiac motion phases may bedenoted by percentage values (e.g., percentage values between 0%-100% asillustrated in FIG. 5B). A phase x % may correspond to a phase angle x%*360°. In some embodiments, the plurality of sampled cardiac motionphases may be obtained at regular intervals (e.g., an interval of 1%,2%, 5%, 10%, 15%, 20%, 25%, etc.). For example, regular intervals may beintervals of 3%, 6%, 9%, 12%, 15%, 18%, 21%, etc. In some embodiments,the plurality of sampled cardiac motion phases may be obtained at randomintervals, irregular intervals, or by a certain preset rule which may bedetermined by a user or the imaging system 100. For example, theplurality of sampled cardiac motion phases may be obtained at the randomintervals from 1% to 100%. As another example, the plurality of sampledcardiac motion phases may be obtained at different intervals fordifferent cardiac cycles.

Specifically, the electrocardiogram (ECG) may refer to a graph ofvoltage versus time. The voltage may be detected from a body surface ofthe object by an electrocardiograph. The ECG may reflect changes inbioelectricity caused by the excitement of the pacemaker, the atria, andthe ventricle of the heart of the object in each cardiac cycle. Phasesof the ECG may indicate the state of the heart in the current cardiaccycle. The heart's ECG may be divided into a plurality of cycles (i.e.,the cardiac cycles) based on an R wave of the ECG. If a current phase isaround 45% of the current cardiac cycle, the heart is usually insystole. If the phase is around 75% of the current cardiac cycle, theheart is usually in diastole. The R wave may correspond to theventricular end-diastole.

FIG. 5B is a schematic diagram illustrating an exemplary ECG signal andcorresponding phases according to some embodiments of the presentdisclosure. As illustrated in FIG. 5B, a cardiac cycle may include aperiod from the beginning of a first Rtag (which represents the positionof the R wave) to the end of a second Rtag next to the first Rtag. Insome embodiments, a cardiac cycle may be divided into 100 phases from 1%to 100%. As illustrated in FIG. 5B, a exemplary regular interval of thesampled cardiac motion phases may be 12.5%, and 8 sampled cardiac motionphases including 12.5%, 25%, 37.5%, 50%, 62.5%, 75%, 87.5%, and 100% maybe obtained. Then imaging data of the 8 sampled cardiac motion phasesmay be reconstructed. In FIG. 5B, the abscissa axis coordinates denotethe ECG phases with the unit of percentage, and the vertical axiscoordinates denote the ECG values with the unit of millivolt. The firstRtag and the second Rtag correspond to points with abscissas of 0% and100%, respectively. The points labelled by asterisks on the abscissaaxis correspond to the 8 sampled cardiac motion phases.

A CT scanner may scan the object continuously for a period of time andobtain scan data. That is, in a cardiac cycle, each phase may correspondto a data set collected by the CT scanner, and 100 phases in a phaserange of 1%-100% may have corresponding data sets in each cardiac cycle.A plurality of cardiac motion phases may be selected at regularintervals. For example, ten phases including 10%, 20%, 30%, 40%, 50%,60%, 70%, 80%, 90%, and 100% may be selected.

In some embodiments, the plurality of sampled cardiac motion phases mayrefer to the plurality of selected cardiac motion phases.

In 5104, a plurality of cardiac images of the plurality of sampledcardiac motion phases may be generated.

In some embodiments, the processing device 140 (e.g., the firstreconstruction module 17200) may perform operation 5104. In someembodiments, the cardiac images of the plurality of sampled cardiacmotion phases may be generated by reconstructing one or more cardiacimages of the each sampled cardiac motion phase. In some embodiments, aplurality of projection data may be generated by an imaging device(e.g., the scanner 110). The plurality of projection data may include aplurality of sub-sets of projection data. In some embodiments, eachsub-set of projection data may correspond to a cardiac motion phase. Insome embodiments, the cardiac images of the each sampled cardiac motionphase may be reconstructed based on one or more sub-sets of projectiondata corresponding to the each sampled cardiac motion phase. In someembodiments, the plurality of cardiac images may include cardiac imagesin a transverse section. In some embodiments, a first portion of theplurality of cardiac images may illustrate a first portion of theobject, while a second portion of the plurality of cardiac images mayillustrate a second portion of the object. For example, one or moreimages may show a first layer of the heart (or chest); one or moreimages may show a second layer of the heart (or chest); one or moreimages may show a third layer of the heart (or chest); one or moreimages may show a fourth layer of the heart (or chest), etc. Thethickness of a layer of the heart (or chest) may be 1 cm, 2 cm, 3 cm,etc. In some embodiments, the thickness may be adjusted according to theneeds or the default setting of the imaging system 100. In someembodiments, the cardiac images may be reconstructed using one or morereconstruction algorithms including, for example, FilteredBack-Projection (FBP), Algebraic Reconstruction Technique (ART), LocalReconstruction Algorithm (Local RA), and ordered-subset expectationmaximization (OSEM), etc.

Specifically, in some embodiments, the images of the plurality of phasesmay be reconstructed according to the scan data of the selected cardiacmotion phases. For example, images of ten phases including 10%, 20%,30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% may be reconstructed.Generally, multiple tomographic images of multiple positions of theheart may be generated by cardiac CT scanning. Therefore, the image(s)corresponding to each phase may refer to an image of a specific positionof the heart corresponding to the each phase, or a sequence of imagesincluding multiple images of multiple positions corresponding to theeach phase.

In 5106, a cardiac motion parameter corresponding to the each sampledcardiac motion phase may be determined.

In some embodiments, the processing device 140 (e.g., the cardiac motionparameter determination module 17300) may perform operation 5106. Insome embodiments, a plurality of cardiac motion parameters may bedetermined based on the plurality of cardiac images of the plurality ofsampled cardiac motion phases. In some embodiments, a sampled cardiacmotion phase may correspond to one or more cardiac images (i.e., the oneor more cardiac images may have a same sampled cardiac motion phase),and a cardiac motion parameter may be determined based on each cardiacimage of the one or more cardiac images, and thus, one or more cardiacmotion parameters corresponding to the sampled cardiac motion phase maybe determined. In some embodiments, a mean cardiac motion parametercorresponding to the sampled cardiac motion phase may be determinedbased on the one or more cardiac motion parameters.

In some embodiments, a cardiac motion parameter may refer to a parameterdescribing the cardiac motion. Exemplary cardiac motion parameters mayinclude a cardiac motion rate, a cardiac motion intensity, etc. Thecardiac motion rate may include a blood flow rate in a blood vessel ofthe heart, a muscle contraction rate of a cardiac muscle, etc. Thecardiac motion intensity may include a magnitude of vasoconstriction, amagnitude of vasodilation, a heartbeat amplitude, etc. In someembodiments, the cardiac motion parameter may refer to a parameterassociated with the cardiac motion rate or the cardiac motion intensity.For example, the parameter may be the cardiac motion rate (or thecardiac motion intensity) multiplied by a coefficient. As anotherexample, the parameter may relate to a reciprocal of the cardiac motionrate (or the cardiac motion intensity). In some embodiments, if thecardiac motion parameter is relatively large, the cardiac motion may berelatively pronounced. In some embodiments, if the cardiac motionparameter is relatively small, the cardiac motion may be relativelypronounced. More descriptions of the determination of the cardiac motionparameters may be found elsewhere in the present disclosure (e.g., FIG.11 and descriptions thereof).

Specifically, in some embodiments, mean absolute difference(s) (MAD(s))between the images of two adjacent sampled cardiac motion phases may bedetermined according to the images of the two adjacent phases and thesizes of the image matrices corresponding to the images. The cardiacmotion parameters of the plurality of sampled cardiac motion phases maybe determined based on the MAD(s) between the images of two adjacentphases. In some embodiments, a set of initial cardiac images of interestmay be determined according to the images corresponding to the pluralityof sampled cardiac motion phases, an average cardiac rate, and/or astandard deviation of cardiac rates; vascular images of interest may beextracted from the set of initial cardiac images of interest; motionrates of a vascular center of a blood vessel (in the vascular images ofinterest) between the plurality of sampled cardiac motion phases may bedetermined according to the vascular images of interest; and the motionrates of the vascular center of the blood vessel may be designated asthe cardiac motion parameter.

In some embodiments, two adjacent sampled cardiac motion phases mayrefer to two sampled cardiac motion phases next to each other withoutanother sampled cardiac motion phase in between in a phase set. Forexample, as illustrated in FIG. 5B, the sampled cardiac motion phase12.5% and the sampled cardiac motion phase 25% are two adjacent sampledcardiac motion phases, i.e., the sampled cardiac motion phase 12.5% andthe sampled cardiac motion phase 25% are adjacent to each other. Asanother example, the sampled cardiac motion phase 25% and the sampledcardiac motion phase 37.5% in FIG. 5B are two adjacent sampled cardiacmotion phases, i.e., the sampled cardiac motion phase 25% and thesampled cardiac motion phase 37.5% are adjacent to each other.

In 5108, a mean phase may be determined based on the plurality ofcardiac motion parameters corresponding to the plurality of sampledcardiac motion phases.

In some embodiments, the processing device 140 (e.g., the mean phasedetermination module 17400) may perform operation 5108. In someembodiments, the mean phase may refer to a relatively optimal phase (inwhich the cardiac motion is relatively slight) for the plurality ofcardiac cycles (in which the projection data of the object aregenerated). In some embodiments, cardiac images of the mean phase mayhave a relatively low level of motion artifacts, a relatively highquality, and/or a relatively high clarity. In some embodiments, the meanphase may be the same as one of the plurality of sampled cardiac motionphases. Alternatively, the mean phase may be different from all theplurality of sampled cardiac motion phases.

For example, if the plurality of sampled cardiac motion phases includeeight phases (see FIG. 5B): p₁ (e.g., 12.5%), p₂ (e.g., 25%), p₃ (e.g.,37.5%), p₄ (e.g., 50%), p₅ (e.g., 62.5%), p₆ (e.g., 75%), p₇ (e.g.,87.5%), and p₈ (e.g., 100%), and each of the plurality of cardiac cycleshas the eight sampled cardiac motion phases, then eight cardiac motionparameters corresponding to the eight sampled cardiac motion phases maybe determined for the cardiac cycles. Furthermore, the mean phase (e.g.,45%, 50%, 75%, etc.) may be determined based on the eight cardiac motionparameters corresponding to the eight sampled cardiac motion phases forthe cardiac cycles. More descriptions of the determination of the meanphase may be found elsewhere in the present disclosure (e.g., FIGS. 6and 12 and descriptions thereof).

Specifically, in some embodiments, the mean phase may be determinedaccording to the cardiac motion parameters of the plurality of sampledcardiac motion phases. In some embodiments, the mean phase may bedetermined based on the motion rates of the vascular center of the bloodvessel between the plurality of sampled cardiac motion phases.

In 5110, target cardiac image(s) of the mean phase may be determined.

In some embodiments, the processing device 140 (e.g., the secondreconstruction module 17500) may perform operation 5110. In someembodiments, the target cardiac image(s) of the mean phase may have arelatively low level of motion artifacts, a relatively high quality,and/or a relatively high clarity.

Specifically, in some embodiments, according to the mean phase, thetarget cardiac image(s) may be selected from the plurality of cardiacimages corresponding to the plurality of sampled cardiac motion phasesthat have been reconstructed. In some embodiments, the scan datacorresponding to the mean phase may be selected from the plurality ofprojection data, and the target cardiac image(s) corresponding to themean phase may be reconstructed according to the scan data. The targetcardiac image(s) may refer to a cardiac image of a specific position ofthe heart corresponding to the mean phase, or a sequence of imagesincluding multiple cardiac images of multiple positions of the heartcorresponding to the mean phase.

In comparison with a process in which a preset phase is selected and animage of the preset phase is reconstructed, the process 500 provided inthis embodiment may determine a mean phase according to a specificsituation of each scan of a patient, and the mean phase may be suitablefor reconstructing an image for the each scan. The reconstructed imagecorresponding to the mean phase may have a relatively high quality witha relatively low level of motion artifacts of the heart.

Alternatively or additionally, in order to further reduce motionartifacts and improve image quality, the following operations may beadded to the process 500:

In 5112, one or more cardiac motion phases may be selected in a presetrange in each cardiac cycle of the plurality of cardiac cycles. Thepreset range may include the mean phase. One or more cardiac images ofthe one or more cardiac motion phases may be reconstructed based on oneor more sub-sets of projection data corresponding to the one or morecardiac motion phases in the each cardiac cycle.

In some embodiments, the processing device 140 (e.g., the secondreconstruction module 17500) may perform operation 5112. In someembodiments, the cardiac motion phases selected in the preset range mayinclude part or all of the phases in the preset range. For example, ifthe mean phase is 45%, and the preset range is 40%-50%, then the cardiacmotion phase(s) in the preset range 40%-50% may be selected (e.g., 41%,42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%).

In some embodiments, the preset range may be 5%, 10%, 20% (or the like)around the mean phase. For example, in each cardiac cycle, phases within10% around the mean phase may be selected, and images corresponding tothe phases within 10% around the mean phase in each cardiac cycle may bereconstructed.

Merely by way of example, if the mean phase is M %, and the preset rangeis 2N %, then the cardiac motion phases from (M−N) % to (M+N) % (i.e.,phases within 2N % around the mean phase) may be selected.

In 5114, a phase of interest may be determined in the each cardiac cyclebased on the one or more cardiac images of the each cardiac cycle, and atarget cardiac image of interest of the phase of interest may beobtained in the each cardiac cycle. Therefore, a sequence of targetcardiac images of interest of the phases of interest may be obtained forthe plurality of cardiac cycles.

In some embodiments, the processing device 140 (e.g., the secondreconstruction module 17500) may perform operation 5114. The phase ofinterest may refer to a relatively optimal phase (in which the cardiacmotion is relatively slight) for the each cardiac cycle. The phases ofinterest for different cardiac cycles may be the same or different. Thephase of interest may be the same as or different from the mean phase.For example, a phase of interest in a first cardiac cycle may be thesame as the mean phase. As another example, a phase of interest in asecond cardiac cycle may be less than the mean phase. As a furtherexample, a phase of interest in a third cardiac cycle may be larger thanthe mean phase. In some embodiments, a first cardiac motion phase in afirst cardiac cycle of the plurality of cardiac cycles may be determinedaccording to the process 500. In some embodiments, a second cardiacmotion phase in a second cardiac cycle of the plurality of cardiaccycles may be determined according to the process 500. In someembodiments, the second cardiac motion phase may be different from thefirst cardiac motion phase. In some embodiments, a first cardiac imageof the first cardiac motion phase in the first cardiac cycle of theplurality of cardiac cycles may be reconstructed. In some embodiments, asecond cardiac image of the second cardiac motion phase in the secondcardiac cycle of the plurality of cardiac cycles may be reconstructed.More descriptions of the determination of the phase of interest may befound elsewhere in the present disclosure (e.g., FIG. 14 anddescriptions thereof).

According to the embodiment, the phase of interest for each cardiaccycle may be determined on the basis of the mean phase, and a targetcardiac image of interest corresponding to the phase of interest may beobtained in each cardiac cycle, and thus, a sequence of target cardiacimages of interest may be obtained. Each image in the sequence of targetcardiac images of interest may have a relatively low level of motionartifacts and a relatively high quality.

For example, projection data of a plurality cardiac cycles may beobtained. For each cardiac cycle, the processing device 140 may obtaineight sampled cardiac motion phases (see FIG. 5B): p₁ (e.g., 12.5%), p₂(e.g., 25%), p₃ (e.g., 37.5%), p₄ (e.g., 50%), p₅ (e.g., 62.5%), p₆(e.g., 75%), p₇ (e.g., 87.5%), and p₈ (e.g., 100%). And the processingdevice 140 may generate cardiac images corresponding to the eightsampled cardiac motion phases by reconstructing the eight cardiac imagesin each cardiac cycle. The processing device 140 may determine a cardiacmotion parameter corresponding to each of the eight sampled cardiacmotion phases based on the cardiac images. Then the processing device140 may determine a mean phase based on the eight cardiac motionparameters, and determine target cardiac images of the mean phase.Furthermore, in some embodiments, the processing device 140 may selectone or more cardiac motion phases in a preset range around the meanphase in each cardiac cycle, and reconstruct the cardiac image(s)corresponding to the selected cardiac motion phases. The processingdevice 140 may determine a phase of interest in each cardiac cycle basedon the cardiac images and obtain a target cardiac image of interest ofthe phase of interest.

In some embodiments, a heart may be further reconstructed based on thesequence of target cardiac images of interest. Because each image in thesequence of target cardiac images of interest may have a relatively lowlevel of motion artifacts and a relatively high quality, thereconstructed heart may have a relatively high quality, which canfacilitate further diagnoses.

It should be noted that, additionally or alternatively, in someembodiments, the mean phase or the phase of interest may also bedetermined based on the image qualities of the cardiac images. In someembodiments, the image qualities of the cardiac images may be determinedaccording to a vessel assessment of the vessel(s) in the cardiac images.More descriptions of the vessel assessment and the determination of themean phase or the phase of interest may be found in, e.g., ChinesePatent Application No. 201811134373.2 entitled “METHODS, APPARATUS,COMPUTING DEVICES AND STORAGE MEDIUMS FOR IMAGE QUALITY ASSESSMENT,”filed Sep. 27, 2018, Chinese Patent Application No. 201811134375.1entitled “METHODS, APPARATUS, COMPUTING DEVICES AND STORAGE MEDIUMS FORIMAGE RECONSTRUCTION,” filed Sep. 27, 2018, and U.S. application Ser.No. 16/437,006 (Attorney Docket No. 20618-0391 US00), entitled “SYSTEMSAND METHODS FOR EVALUATING IMAGE QUALITY,” filed Jun. 11, 2019, thecontents of which are hereby incorporated by reference.

FIG. 6 is a flowchart illustrating an exemplary process for determininga mean phase according to some embodiments of the present disclosure. Insome embodiments, operation 5106 illustrated in FIG. 5 may be performedaccording to operations 6202 and 6204 in the process 600. In someembodiments, the process 600 may be executed by the imaging system 100.For example, the process 600 may be implemented as a set of instructions(e.g., an application) stored in one or more storage devices (e.g., thestorage device 150, the storage 220, and/or the storage 390) and invokedand/or executed by the processing device 140 (implemented on, forexample, the processor 210 of the computing device 200, and the CPU 340of the mobile device 300). The operations of the process 600 presentedbelow are intended to be illustrative. In some embodiments, the processmay be accomplished with one or more additional operations notdescribed, and/or without one or more of the operations discussed.Additionally, the order in which the operations of the process 600 asillustrated in FIG. 6 and described below is not intended to belimiting.

In an embodiment, as shown in FIG. 6 , an exemplary process fordetermining a mean phase is provided. The process 600 may include thefollowing operations:

In 6202, a plurality of mean absolute differences (MADs) may be obtainedby determining an MAD between two cardiac images of each two adjacentsampled cardiac motion phases.

In some embodiments, the processing device 140 (e.g., cardiac motionparameter determination module 17300) may perform operation 6202. Insome embodiments, the number or count of the plurality of MADs may beequal to or less than the number or count of the plurality of sampledcardiac motion phases. For example, if the plurality of sampled cardiacmotion phases include eight phases (see FIG. 5B): p₁ (e.g., 12.5%), p₂(e.g., 25%), p₃ (e.g., 37.5%), p₄ (e.g., 50%), p₅ (e.g., 62.5%), p₆(e.g., 75%), p₇ (e.g., 87.5%), and p₈ (e.g., 100%), then a first MAD maybe determined between two cardiac images of two adjacent sampled cardiacmotion phases p₁ and p₂, a second MAD may be determined between twocardiac images of two adjacent sampled cardiac motion phases p₂ and p₃,a third MAD may be determined between two cardiac images of two adjacentsampled cardiac motion phases p₃ and p₄, a fourth MAD may be determinedbetween two cardiac images of two adjacent sampled cardiac motion phasesp₄ and p₅, a fifth MAD may be determined between two cardiac images oftwo adjacent sampled cardiac motion phases p₅ and p₆, a sixth MAD may bedetermined between two cardiac images of two adjacent sampled cardiacmotion phases p₆ and p₇, and a seventh MAD may be determined between twocardiac images of two adjacent sampled cardiac motion phases p₇ and ixIn some embodiments, an eighth MAD may be determined between two cardiacimages of two adjacent sampled cardiac motion phases p₈ and p₁.

Specifically, in some embodiments, before the MAD between the twocardiac images of the each two adjacent sampled cardiac motion phases isdetermined, the cardiac images corresponding to the plurality of sampledcardiac motion phases may be preprocessed. In some embodiments, thepreprocessing may include: performing image segmentation on the cardiacimages of the plurality of sampled cardiac motion phases according toone or more thresholds; and removing one or more regions that areunrelated to cardiac motion to obtain images of one or more regionsrelating to cardiac motion.

In some embodiments, the threshold(s) may relate to gray levels ofpixels or voxels of the cardiac images. In some embodiments, thethreshold(s) may be determined by the imaging system 100, or may bepreset by a user or operator via the terminal(s) 130.

In an embodiment, the image segmentation of the cardiac images of theplurality of sampled cardiac motion phases based on the threshold(s) maybe represented as:

$\begin{matrix}{{A\left( {i,j} \right)} = \left\{ {\begin{matrix}A & {{{if}A\left( {i,j} \right)} > {ConThre}} \\0 & {{{if}A\left( {i,j} \right)} \leq {ConThre}}\end{matrix},} \right.} & {{Equation}(1)}\end{matrix}$

where A is a matrix of the gray level(s) of the pixels in a cardiacimage of a sampled cardiac motion phase; ConThre is the threshold;A(i,j) is the gray level of a pixel with a coordinate (i,j) in thecardiac image.

In an embodiment, the MAD between the two cardiac images of the each twoadjacent sampled cardiac motion phases may be determined as:

$\begin{matrix}{{{MAD\left( {A,B} \right)} = {\frac{1}{{Mm}.{\,^{\hat{}}2}}{\sum_{i}^{Mm}{\sum_{j}^{Mm}{❘{{A\left( {i,j} \right)} - {B\left( {i,j} \right)}}❘}}}}},} & {{Equation}(2)}\end{matrix}$

where A and B represent the cardiac images of the each two adjacentsampled cardiac motion phases, respectively; A(i, j) is the gray levelof a pixel with a coordinate (i, j) in the image A; B(i, j) is the graylevel of a pixel with a coordinate (i, j) in the image B; Mm is the sizeof the image matrix A and/or B; MAD(A, B) is the mean absolutedifference between images A and B.

In 6204, the plurality of cardiac motion parameters corresponding to theplurality of sampled cardiac motion phases may be determined based onthe plurality of MADs.

In some embodiments, the processing device 140 (e.g., the cardiac motionparameter determination module 17300) may perform operation 6204.

Specifically, in some embodiments, an MAD between a cardiac image of asampled cardiac motion phase and a cardiac image of a previous sampledcardiac motion phase may be obtained as a first parameter. In someembodiments, an MAD between a cardiac image of a sampled cardiac motionphase and a cardiac image of a next sampled cardiac motion phase may beobtained as a second parameter. In some embodiments, the first parameterand the second parameter of the same cardiac image may be added toobtain a cardiac motion parameter of the sampled cardiac motion phase.

In some embodiments, the processing device 140 may determine a first MADbetween a first cardiac image of a first sampled cardiac motion phasethat occurs before the sampled cardiac motion phase and a cardiac imageof the sampled cardiac motion phase. In some embodiments, the processingdevice 140 may determine a second MAD between a second cardiac image ofa second sampled cardiac motion phase that occurs after the sampledcardiac motion phase and the cardiac image of the sampled cardiac motionphase. In some embodiments, the processing device 140 may furtherdesignate a sum of the first MAD and the second MAD as the cardiacmotion parameter corresponding to the sampled cardiac motion phase. Insome embodiments, the first sampled cardiac motion phase may be adjacentto the sampled cardiac motion phase. In some embodiments, the secondsampled cardiac motion phase may be adjacent to the sampled cardiacmotion phase. In some embodiments, the sampled cardiac motion phases (ina same cycle or different cycles) may be arranged based on theirrespective sequence numbers, e.g., in an ascending order. The sequencenumber of a sampled cardiac motion phase may be determined based on thetiming of the sampled cardiac motion phase in the cycle in which thesampled cardiac motion phase occurs relative to a reference time pointof the cycle. Exemplary reference time points of a cycle of the cardiacmotion may include the beginning of the cardiac cycle (e.g., the time ofcontraction of the atria), the end of the cardiac cycle (e.g., the timeof ventricular relaxation), or a midpoint of the cardiac cycle (e.g.,the beginning of the ventricular systole). Sampled cardiac motion phasesthat occur in different cycles of cardiac motion may have a samesequence number. If a sequence number of a sampled cardiac motion phaseA is lower than a sequence number of a sampled cardiac motion phase B,then the sampled cardiac motion phase A may be considered “occur before”the sampled cardiac motion phase B, and accordingly, the sampled cardiacmotion phase B may “occur after” the sampled cardiac motion phase A. Ifthe absolute value of a difference between sequence numbers of twosampled cardiac motion phases C and D is 1, then the sampled cardiacmotion phase C and the sampled cardiac motion phase D are considered“adjacent to” each other.

In an embodiment, the determination of the cardiac motion parameter of asampled cardiac motion phase may be represented as:

ΔM(P _(l) ,k)=MAD(V _(k)(P _(l) ,i,j),(V _(k)(P _(l−1) ,i,j))+MAD(V_(k)(P _(l) ,i,j),V _(k)(P _(l+1) ,i,j)),  Equation (3)

where MAD(V_(k)(P_(l), i, j),V_(k)(P_(l−1), i, j)) is the mean absolutedifference between a cardiac image V_(k)(P_(l), i, j) of a currentsampled cardiac motion phase and a cardiac image V_(k)(P_(l−1), i, j) ofa sampled cardiac motion phase that occurs before the current sampledcardiac motion phase; MAD(V_(k)(P_(l), i, j), V_(k)(P_(l+1), i, j)) isthe mean absolute difference between the cardiac image V_(k)(P_(l), i,j) of the current sampled cardiac motion phase and a cardiac imageV_(k)(P_(l+1), i, j) of a sampled cardiac motion phase that occurs afterthe current sampled cardiac motion phase; ΔM(P_(l), k) is the cardiacmotion parameter corresponding to the cardiac image of the currentsampled cardiac motion phase.

In Equation (3), P_(l) is the current sampled cardiac motion phase, l isa sequence number of the current sampled cardiac motion phase in theplurality of sampled cardiac motion phases, k is a sequence number of aslice of the object, i and j represent the element locations in acorresponding cardiac image. In some embodiments, the number (or count)of the cardiac motion parameters (e.g., the ΔM(P_(l), k) in Equation(3)) may be less than the number (or count) of the sampled cardiacmotion phases.

In 6206, a mean phase may be determined based on the plurality ofcardiac motion parameters corresponding to the plurality of sampledcardiac motion phases.

In some embodiments, the processing device 140 (e.g., the mean phasedetermination module 17400) may perform operation 6206.

Specifically, in some embodiments, in a systolic period of cardiacmotion, a cardiac motion phase corresponding to a minimum cardiac motionparameter in the systolic period may be designated as the mean phase inthe systolic period. In a diastolic period of cardiac motion, a cardiacmotion phase corresponding to a minimum cardiac motion parameter in thediastolic period may be designated as the mean phase in the diastolicperiod.

In some embodiments, the cardiac motion parameter(s) determined based onMAD(s) may represent the difference(s) between cardiac images. Arelatively small cardiac motion parameter may represent a relativelysmall motion amplitude, indicating that the cardiac motion is relativelysmooth. Accordingly, the phase corresponding to a minimum motionparameter may be determined as the mean phase, and the cardiac imagereconstructed under the mean phase may have less artifact(s).

In some embodiments, in a systolic period of cardiac motion, a cardiacmotion phase corresponding to a maximum cardiac motion parameter in thesystolic period may be designated as the mean phase in the systolicperiod. In a diastolic period of cardiac motion, a cardiac motion phasecorresponding to a maximum cardiac motion parameter in the diastolicperiod may be designated as the mean phase in the diastolic period. Forexample, as illustrated in FIG. 12 , a relatively large motion parametermay represent a relatively stable motion state, indicating that thecardiac motion is relatively smooth. Accordingly, the phasecorresponding to a maximum motion parameter may be determined as themean phase, and the cardiac image reconstructed under the mean phase mayhave less artifact(s). In some embodiments, one or more mean phases maybe determined based on the plurality of cardiac motion parameterscorresponding to the plurality of sampled cardiac motion phases. Forexample, a first mean phase may be determined in the systolic period,and a second mean phase may be determined in the diastolic period. Insome embodiments, the one or more mean phases may be provided to a useror an operator, and the user or operator may choose a mean phase forcardiac image reconstruction. If two or more mean phases are determined,then two or more preset ranges may be selected, and accordingly, two ormore phases of interest may be determined. Specifically, in someembodiments, a preset range may be selected for each mean phase, and aphase of interest may be determined in each preset range. In someembodiments, the phase(s) of interest may be provided to the user oroperator, and the user or operator may determine a phase of interest ineach cardiac cycle for cardiac image reconstruction.

In some embodiments, the determination of the mean phase in the systolicperiod may be represented as:

P _(Basic)1=arg_(l) min(Σ_(k) ^(N) ΔM(P _(l) ,k)/N), for all P _(l)where P _(1S) ≤P _(l) ≤P _(1E),  Equation (4)

where P_(Basic)1 is the mean phase in the systolic period; Nis thenumber (or count) of cardiac images of the sampled cardiac motion phasesin the systolic period; (P_(1S)≤P_(l)≤P_(1E)) is the range of thesampled cardiac motion phases in the systolic period.

In Equation (4), P_(1E) is the end phase in the systolic period, andP_(1S) is the start phase in the systolic period.

In an embodiment, the determination of the mean phase in the diastolicperiod may be represented as:

P _(Basic)2=arg_(l) min(Σ_(k) ^(N) ΔM(P _(l) ,k)/N), for all P _(l)where P _(2S) ≤P _(l) ≤P _(2E),  Equation (5)

where P_(Basic)2 is the mean phase in the diastolic period; N is thenumber (or count) of cardiac images of the sampled cardiac motion phasesin the diastolic period; (P_(2S)≤P_(l)≤P_(2E)) is the range of sampledcardiac motion phases in the diastolic period.

In Equation (5), P_(2E) is the end phase in the diastolic period, P_(2S)is the start phase in the diastolic period.

According to the process for determining the mean phase described above,the cardiac motion parameters of the corresponding sampled cardiacmotion phases may be determined based on the mean absolute differencesbetween each two cardiac images of each two adjacent sampled cardiacmotion phases, and the cardiac motion phase with the minimum (ormaximum) cardiac motion parameter may be designated as the mean phase.Therefore, the mean phase may be determined accurately, and the accuracyof the determination of the optimal phase in the cardiac motion may beensured.

In some embodiments, the cardiac motion parameter described in FIG. 6may be regarded as a first motion parameter.

FIG. 7 is a flowchart illustrating another exemplary process fordetermining a mean phase according to some embodiments of the presentdisclosure. In some embodiments, operation 5106 illustrated in FIG. 5may be performed according to the process 700. In some embodiments, theprocess 700 may be executed by the imaging system 100. For example, theprocess 700 may be implemented as a set of instructions (e.g., anapplication) stored in one or more storage devices (e.g., the storagedevice 150, the storage 220, and/or the storage 390) and invoked and/orexecuted by the processing device 140 (implemented on, for example, theprocessor 210 of the computing device 200, and the CPU 340 of the mobiledevice 300). The operations of the process 700 presented below areintended to be illustrative. In some embodiments, the process may beaccomplished with one or more additional operations not described,and/or without one or more of the operations discussed. Additionally,the order in which the operations of the process 700 as illustrated inFIG. 7 and described below is not intended to be limiting.

In an embodiment, as shown in FIG. 7 , another exemplary process fordetermining the mean phase is provided. The process 700 may include thefollowing operations:

In 7302, a set of initial cardiac images of interest may be determinedbased on the one or more cardiac images of the sampled cardiac motionphase(s), an average cardiac rate, and/or a cardiac rate fluctuation.

In some embodiments, the processing device 140 (e.g., the cardiac motionparameter determination module 17300) may perform operation 7302. Insome embodiments, cardiac image(s) with a relatively high quality may beselected from the plurality of cardiac images of the plurality ofsampled cardiac motion phases. In some embodiments, the cardiac image(s)with the relatively high quality may be determined as the set of initialcardiac images of interest. In some embodiments, the qualities of thecardiac images may relate to the cardiac motion phases, the averagecardiac rate, and/or the cardiac rate fluctuation. In some embodiments,the cardiac rate fluctuation may be represented by a standard deviationof cardiac rates.

Specifically, in some embodiments, if the phase is in the vicinity of45%, the heart may usually be in the systolic period; if the phase is inthe vicinity of 75%, the heart may usually be in the diastolic period.Therefore, the two phases may be often used clinically as the phase(s)for cardiac image reconstruction. If the average cardiac rate isrelatively stable, the quality of the cardiac image(s) of the phase inthe vicinity of 75% may be relatively high. If the average cardiac rateis relatively rapid, the quality of the cardiac image(s) of the phase inthe vicinity of 45% may be relatively high.

In some embodiments, if the mean cardiac rate is relatively stable, thecardiac rate fluctuation may be relatively low. In some embodiments, ifthe average cardiac rate is relatively rapid, the average cardiac ratemay be relatively high. Merely by way of example, if the average cardiacrate is larger than 70, and/or the standard deviation of cardiac ratesis larger than 1, cardiac image(s) of a sampled cardiac motion phase(e.g., 50%) may be determined as the set of initial cardiac images ofinterest; otherwise, cardiac image(s) of a sampled cardiac motion phase(e.g., 75%) may be determined as the set of initial cardiac images ofinterest. In some embodiments, the cardiac motion phase(s) correspondingto the set of initial cardiac images of interest may be regarded asinitial optimal phase(s).

In 7304, one or more vascular images of interest may be extracted fromthe set of initial cardiac images of interest.

In some embodiments, the processing device 140 (e.g., the cardiac motionparameter determination module 17300) may perform operation 7304.

Specifically, in some embodiments, according to the set of initialcardiac images of interest, ventricular image(s) may be extracted fromthe set of initial cardiac images of interest, a threshold associatedwith gray level(s) of a contrast agent may be determined according tothe ventricular image(s). The ventricular image(s) may be segmentedbased on the threshold associated with the gray level(s) of the contrastagent to obtain contrast agent image(s). Then, the vascular image(s) ofinterest may be extracted from the contrast agent image(s). In the fieldof medical imaging technology, in order to enhance the imaging effect ofa target site of a patient, a contrast agent may be usually injected oradministered to the target site. Accordingly, the set of initial cardiacimages of interest may be image(s) generated after the injection oradministration of the contrast agent.

In some embodiments, the vascular image(s) of interest may be extractedfrom the plurality of cardiac images of the plurality of sampled cardiacmotion phases. Merely by way of example, ventricular image(s) may beextracted from the set of initial cardiac images of interest; athreshold associated with gray level(s) of a contrast agent may bedetermined according to the ventricular image(s); the ventricularimage(s) may be segmented based on the threshold associated with thegray level(s) of the contrast agent to obtain contrast agent image(s);then, the ventricular image(s), contrast agent image(s), and/or thethreshold may be applied to the plurality of cardiac images of theplurality of sampled cardiac motion phases to obtain vascular image(s)of interest. As another example, ventricular image(s) may be extractedfrom the plurality of cardiac images of the plurality of sampled cardiacmotion phases; a threshold associated with gray level(s) of a contrastagent may be determined according to the ventricular image(s); theventricular image(s) may be segmented based on the threshold associatedwith the gray level(s) of the contrast agent to obtain contrast agentimage(s); then, vascular image(s) of interest may be extracted from thecontrast agent image(s). More descriptions of the extraction of thevascular image(s) of interest may be found elsewhere in the presentdisclosure (e.g., FIGS. 8-10 and descriptions thereof).

In 7306, the cardiac motion parameter corresponding to the each sampledcardiac motion phase may be determined based on the one or more vascularimages of interest.

In some embodiments, the processing device 140 (e.g., the cardiac motionparameter determination module 17300) may perform operation 7306.

Specifically, in some embodiments, the vascular center(s) in thevascular image(s) of interest may be determined; positions of thevascular centers of two adjacent sampled cardiac motion phases may becompared to obtain a displacement between the positions of the vascularcenters of the two adjacent sampled cardiac motion phases in theplurality of sampled cardiac motion phases; and then a sampling interval(or a time interval) between the two adjacent sampled cardiac motionphases in the plurality of sampled cardiac motion phases may beobtained. The displacement between the vascular centers of the twoadjacent sampled phases may be divided by the corresponding samplinginterval to obtain a motion rate of the vascular center.

Similarly, motion rates of vascular center(s) for one or more sampledcardiac motion phases may be determined. In some embodiments, the motionrate(s) may be determined as the cardiac motion parameter(s)corresponding to the sampled cardiac motion phase(s). More descriptionsof the determination of the cardiac motion parameter(s) may be foundelsewhere in the present disclosure (e.g., FIG. 11 and descriptionsthereof).

According to the process of determining the mean phase described above,initial optimal phase(s) may be determined according to the averagecardiac rate and the cardiac rate fluctuation; region(s) of interest maybe extracted from the set of initial cardiac images of interest; motionrate(s) of vascular center(s) between the sampled cardiac motionphase(s) may be determined according to the vascular image(s) ofinterest; and the mean phase may be determined according to the motionrate(s) of the vascular center(s) between the sampled cardiac motionphase(s). Therefore, the mean phase may be accurately determined, andthe accuracy of the determination of the optimal phase in the cardiacmotion may be ensured.

FIG. 8 is a flowchart illustrating an exemplary process for extractingvascular image(s) of interest according to some embodiments of thepresent disclosure. In some embodiments, operation 7304 illustrated inFIG. 7 may be performed according to the process 800. In someembodiments, the process 800 may be executed by the imaging system 100.For example, the process 800 may be implemented as a set of instructions(e.g., an application) stored in one or more storage devices (e.g., thestorage device 150, the storage 220, and/or the storage 390) and invokedand/or executed by the processing device 140 (implemented on, forexample, the processor 210 of the computing device 200, and the CPU 340of the mobile device 300). The operations of the process 800 presentedbelow are intended to be illustrative. In some embodiments, the processmay be accomplished with one or more additional operations notdescribed, and/or without one or more of the operations discussed.Additionally, the order in which the operations of the process 800 asillustrated in FIG. 8 and described below is not intended to belimiting.

In an embodiment, as shown in FIG. 8 , an exemplary process forextracting vascular image(s) of interest is provided. The process 800may include the following operations:

In 8402, one or more ventricular images may be extracted from the set ofinitial cardiac images of interest.

In some embodiments, the processing device 140 (e.g., the cardiac motionparameter determination module 17300) may perform operation 8402. Insome embodiments, the ventricular image(s) may be extracted from theplurality of cardiac images of the plurality of sampled cardiac motionphases. The process of the extraction of the ventricular image(s) fromthe plurality of cardiac images of the plurality of sampled cardiacmotion phases may be similar to the process illustrated below (e.g., theoperations 8402-8408).

Specifically, in some embodiments, according to the average cardiac rateand the cardiac rate fluctuation, the set of initial cardiac images ofinterest may be selected from the cardiac image(s) of the sampledcardiac motion phase(s); according to a threshold associated with graylevel(s) of bone(s), bone image(s) that have elements (e.g., pixels orvoxels) with gray level(s) larger than the threshold may be extracted. Amaximum intensity projection may be performed on the bone image(s) in anaxial direction of a thoracic cavity, and a maximum intensity projectionimage of the bone image(s) may be obtained. The maximum intensityprojection(s) may be generated based on element(s) having a maximumintensity (or density) along each projection ray directed to thepatient's target site. That is, if the projection ray passes through theset of initial cardiac images of interest, the element(s) with thehighest intensity (or density) in the image(s) may be retained andprojected onto a two-dimensional plane, thereby forming a maximumintensity projection image of the bone image(s). According to themaximum intensity projection image of the bone image(s), the maximumintensity projection image (e.g., elements of the maximum intensityprojection image) of the bone image(s) may correspond to differentBoolean values. A thoracic contour boundary may be determined accordingto boundaries of the different Boolean values. Elements within thethoracic contour boundary may be extracted from the set of initialcardiac images of interest to obtain a pleural image (or thoraciccontour image). Then, connected domain(s) may be determined based on apleural image; a target connected domain with a maximum number ofelements among the connected domain(s) may be extracted as a ventricularimage. A connected domain may correspond to a region in a complex plane.If a simple closed curve is used in the complex plane, and the internalof the closed curve always belongs to the region, then the region is aconnected domain.

In some embodiments, each element of an image may have a value (e.g., agray level, a CT value, etc.). More descriptions of the extraction ofthe ventricular image(s) may be found elsewhere in the presentdisclosure (e.g., FIG. 9 and descriptions thereof).

In 8404, a first threshold associated with gray level(s) of a contrastagent may be determined based on the one or more ventricular images.

In some embodiments, the processing device 140 (e.g., the cardiac motionparameter determination module 17300) may perform operation 8404.

Specifically, in some embodiments, gradient image(s) corresponding tothe ventricular image(s) may be determined based on the ventricularimage(s). In image processing, modulus (or moduli) of gradient(s) may besimply referred as gradient(s), and an image using the gradient(s) aselements may be referred as a gradient image. If an image (e.g., aventricular image) includes an edge (e.g., of different portions of anobject), a corresponding gradient image may include relatively largegradient value(s). If the image includes a relatively smooth part, anddifference(s) between gray level(s) are relatively low, then thecorresponding gradient value(s) are relatively low. The gray level(s) ofthe element(s) in the gradient image may be analyzed statistically, anda target ventricular image whose corresponding gradient image haselements with values larger than a proportional threshold may bedetermined as a marker image. The threshold associated with the graylevel(s) of the contrast agent may be determined based on the value(s)of the element(s) of the marker image using the OTSU algorithm. The OTSUalgorithm is an efficient algorithm for the binarization of image(s),using a threshold to segment an original image into a foreground imageand a background image. An optimal segment threshold may be taken as thethreshold associated with the gray level(s) of the contrast agent.

In some embodiments, the proportional threshold may be a predeterminedvalue which is an empirical value or an appropriate value determined bythe system. More descriptions of the determination of the thresholdassociated with the gray level(s) of the contrast agent may be foundelsewhere in the present disclosure (e.g., FIG. 10 and descriptionsthereof).

In 8406, one or more contrast agent images may be obtained by segmentingthe one or more ventricular images based on the first threshold.

In some embodiments, the processing device 140 (e.g., the cardiac motionparameter determination module 17300) may perform operation 8406.

Specifically, in some embodiments, the image segmentation may beperformed based on the threshold associated with the contrast agent(e.g., the first threshold). Element(s) with gray level(s) greater thanthe threshold associated with the contrast agent may be extracted fromthe ventricular image(s) to obtain the contrast agent image(s).

In 8408, the one or more vascular images of interest may be extractedbased on the one or more contrast agent images.

In some embodiments, the processing device 140 (e.g., the cardiac motionparameter determination module 17300) may perform operation 8408.

Specifically, in some embodiments, the right coronary artery is anarterial blood vessel that is clinically more visible than other bloodvessels, the motion of the right coronary artery may reflect the motionof the heart, and then the motion of the heart in different phases maybe determined by detecting the motion of the right coronary artery inthe corresponding phases. In some embodiments, in the contrast agentimage(s), image(s) having a relatively low amount of elements associatedwith the contrast agent and having elements with relatively lowextravascular CT values may be extracted from portion(s) of the contrastagent image(s) corresponding to the upper left half of the ventricle toobtain the vascular image(s) of interest.

According to the process for extracting the vascular image(s) ofinterest described above, ventricular image(s) may be extracted from theset of initial cardiac images of interest; the threshold associated withgray level(s) of the contrast agent may be determined according to theventricular image(s); the ventricular image(s) may be segmented based onthe threshold associated with the contrast agent to obtain the contrastagent image(s); and the vascular image(s) of interest may be extractedfrom the contrast agent image(s). Therefore, right coronary vascularimage(s) may be determined accurately in the set of initial cardiacimages of interest, thereby improving the accuracy of the determinationof the motion rate(s) of the vascular center(s), and improving theaccuracy of the determination of the optimal phase in the cardiacmotion.

It should be noted that, in some embodiments, the vascular image(s) ofinterest may be extracted from the plurality of cardiac images of theplurality of sampled cardiac motion phases. For example, the ventricularimage(s) extracted in 8402, the first threshold determined in 8404,and/or the contrast agent image(s) obtained in 8406 may be applied tothe plurality of cardiac images of the plurality of sampled cardiacmotion phases to obtain the vascular image(s) of interest.

FIG. 9 is a flowchart illustrating an exemplary process for obtainingventricular image(s) according to some embodiments of the presentdisclosure. In some embodiments, operation 8402 illustrated in FIG. 8may be performed according to the process 900. In some embodiments, theprocess 900 may be executed by the imaging system 100. For example, theprocess 900 may be implemented as a set of instructions (e.g., anapplication) stored in one or more storage devices (e.g., the storagedevice 150, the storage 220, and/or the storage 390) and invoked and/orexecuted by the processing device 140 (implemented on, for example, theprocessor 210 of the computing device 200, and the CPU 340 of the mobiledevice 300). The operations of the process 900 presented below areintended to be illustrative. In some embodiments, the process may beaccomplished with one or more additional operations not described,and/or without one or more of the operations discussed. Additionally,the order in which the operations of the process 900 as illustrated inFIG. 9 and described below is not intended to be limiting.

In an embodiment, as shown in FIG. 9 , an exemplary process forextracting ventricular image(s) is provided. The process 900 may includethe following operations:

In 9502, one or more bone images may be extracted from the set ofinitial cardiac images of interest and based on a second thresholdassociated with gray level(s) of one or more bones in the set of initialcardiac images of interest.

In some embodiments, the processing device 140 (e.g., the cardiac motionparameter determination module 17300) may perform operation 9502. Insome embodiments, the bone image(s) may be extracted from the pluralityof cardiac images of the plurality of sampled cardiac motion phasessimilarly.

Specifically, in some embodiments, according to the average cardiac rateand the cardiac rate fluctuation, the set of initial cardiac images ofinterest may be selected from the cardiac image(s) of the sampledcardiac motion phase(s); according to the threshold associated with thebone(s) (e.g., the second threshold), bone image(s) that have elementswith gray level(s) larger than the threshold associated with the bone(s)may be extracted. The threshold associated with the bone(s) in thethoracic cavity may be generally 1500 HU according to the clinicalexperience. That is, region(s) including elements with gray level(s)greater than 1500 HU may be extracted from the set of initial cardiacimages of interest and may be regarded as the bone image(s).

In an embodiment, the bone image(s) may be determined according to thefollowing equations:

$\begin{matrix}{{BoneImg} = \left\{ {\begin{matrix}{{OriImg},} & {{{if}{OriImg}} > {BoneThre}} \\0 & {else}\end{matrix},} \right.} & {{Equation}(6)}\end{matrix}$ $\begin{matrix}{{{BoneImgSet} = {{{BoneImg}_{i}i} = {1,2}}},\ldots,{ImgNum},} & {{Equation}(7)}\end{matrix}$

where BoneImg is a bone image, BoneThre is the threshold associated withbone(s), OriImg represents the set of initial cardiac images ofinterest, and BoneImgSet is a set of bone images.

In Equation (7), ImgNum is the number of the bone images.

In 9504, a maximum intensity projection image of the one or more boneimages may be obtained by performing a maximum intensity projection onthe one or more bone images.

In some embodiments, the processing device 140 (e.g., the cardiac motionparameter determination module 17300) may perform operation 9504.

Specifically, in some embodiments, the maximum intensity projectionimage may be generated based on element(s) having a maximum intensity(or density) along each projection ray directed to the target site ofthe patient. That is, if the projection ray passes through the set ofinitial cardiac images of interest, the element(s) with the highestintensity (or density) in the image(s) may be retained and projectedonto a two-dimensional plane, thereby forming a maximum intensityprojection image of the bone image(s).

In an embodiment, the maximum intensity projection image of the boneimage(s) may be determined according to the following equations:

BoneMIP_(axial)=MIP(BoneImgSet),  Equation (8)

where BoneImgSet is a set of bone image(s), MIP is a maximum intensityprojection operation, and BoneMIP_(axial) is a maximum intensityprojection image of the set of bone images.

In Equation (8), axial refers that the maximum intensity projection isperformed on the bone image(s) in an axial direction of a thoraciccavity.

In 9506, a thoracic contour boundary may be determined for the maximumintensity projection (MIP) image.

In some embodiments, the processing device 140 (e.g., the cardiac motionparameter determination module 17300) may perform operation 9506.

Specifically, in some embodiments, according to the maximum intensityprojection image of the bone image(s), the Boolean value of elements ina ventricular region of the maximum intensity projection image of thebone image(s) may be set as 1, and the Boolean value of elements in thenon-ventricular region of the maximum intensity projection image of thebone image(s) may be set as 0. A boundary of elements with Boolean value1 and elements with Boolean value 0 may be taken as a thoracic contourboundary.

In some embodiments, a thoracic contour boundary may correspond to abinary image, wherein elements inside the thoracic contour boundary mayhave the Boolean value 1, while elements outside the thoracic contourboundary may have the Boolean value 0. In some embodiments, the thoraciccontour boundary may include one or more elements representing one ormore positions of a thoracic contour boundary of an object. Moredescriptions of the position(s) of the thoracic contour boundary may befound elsewhere in the present disclosure (e.g., FIGS. 18 and 20 anddescriptions thereof).

In an embodiment, the thoracic contour boundary may be determined as:

Boundary=CalBoundary(BoneMIP_(axial)),  Equation (9)

where Boundary represents elements with Boolean values, BoneMIP_(axial)is the maximum intensity projection image of the bone image(s), andCalBoundary is an operation that sets the Boolean value as 1 or 0according to whether it is a ventricular region.

In 9508, the one or more ventricular images may be obtained based on thethoracic contour boundary and/or the set of initial cardiac images ofinterest.

In some embodiments, the processing device 140 (e.g., the cardiac motionparameter determination module 17300) may perform operation 9508.

Specifically, in some embodiments, a pleural image may be obtainedaccording to the set of initial cardiac images of interest and thethoracic contour boundary. Connected domain(s) may be determinedaccording to the pleural image, and a target connected domain with amaximum number of elements among the connected domain(s) may beidentified as a ventricular mask image.

According to the set of initial cardiac images of interest and thethoracic contour boundary, a pleural image may be obtained. A regionwithin a thoracic contour boundary may be extracted as a pleural imagein one of the set of initial cardiac images of interest. That is, aregion that has specific elements may be extracted as a pleural image inthe set of initial cardiac images of interest. The specific elements mayhave value(s) larger than a threshold associated with soft tissue(s) andmay have a Boolean value 1.

In some embodiments, a pleural image may be a binary image associatedwith the thoracic contour boundary. In some embodiments, the specificelements may have value(s) larger than the threshold associated withsoft tissue(s) and may correspond to elements having a Boolean value 1in the thoracic contour boundary. In some embodiments, the thresholdassociated with soft tissue(s) may be a default value determined by theimaging system 100 or preset by a user or operator via the terminal(s)130.

In an embodiment, the pleural image may be extracted according to thefollowing equation:

$\begin{matrix}{{MaskImg} = \left\{ {\begin{matrix}1 & {{{OriImg} > {{SoftTisThre}{and}{Boundary}}} = 1} \\0 & {else}\end{matrix},} \right.} & {{Equation}(10)}\end{matrix}$

where MaskImg is a pleural image, Boundary represents elements withBoolean values, and SoftTisThre is a threshold associated with softtissue(s).

Connected domain(s) may be determined according to the pleural image,and a target connected domain with a maximum number of elements amongthe connected domain(s) may be designated as a ventricular mask image.Based on the pleural image, the target connected domain with the maximumnumber of elements may be designated as the ventricular mask image. Aconnected domain may correspond to a region in a complex plane. If asimple closed curve is used in the complex plane, and the internal ofthe closed curve always belongs to the region, then the region is aconnected domain.

In some embodiments, the ventricular mask image may be a binary imageassociated with the ventricular region of the object. In someembodiments, the ventricular image(s) may be obtained based on theventricular mask image and the set of initial cardiac images ofinterest. In some embodiments, the ventricular image(s) may be obtaineddirectly from the set of initial cardiac images of interest.

According to the process for extracting the ventricular image(s), imagesegmentation may be performed on the initial cardiac image(s) ofinterest based on a threshold associated with bone(s) to obtain boneimage(s); a maximum intensity projection may be performed on the boneimage(s) to obtain a maximum intensity projection image. The process mayfurther include determining a thoracic contour boundary according to themaximum intensity projection image, extracting a region within thethoracic contour boundary as a pleural image, determining connecteddomain(s) of the pleural image, and designating a target connecteddomain with the maximum number of elements as the ventricular maskimage. Therefore, the thoracic contour boundary may be accuratelydetermined, thereby improving the accuracy of the determination of theventricular image(s), and improving the accuracy of the determination ofthe heart region.

FIG. 10 is a flowchart illustrating an exemplary process for extractingvascular image(s) of interest according to some embodiments of thepresent disclosure. In some embodiments, operation 8404 illustrated inFIG. 8 may be performed according to the operations 10602-10606 inprocess 1000. In some embodiments, the process 1000 may be executed bythe imaging system 100. For example, the process 1000 may be implementedas a set of instructions (e.g., an application) stored in one or morestorage devices (e.g., the storage device 150, the storage 220, and/orthe storage 390) and invoked and/or executed by the processing device140 (implemented on, for example, the processor 210 of the computingdevice 200, and the CPU 340 of the mobile device 300). The operations ofthe process 1000 presented below are intended to be illustrative. Insome embodiments, the process may be accomplished with one or moreadditional operations not described, and/or without one or more of theoperations discussed. Additionally, the order in which the operations ofthe process 1000 as illustrated in FIG. 10 and described below is notintended to be limiting.

In an embodiment, as shown in FIG. 10 , an exemplary process fordetermining a threshold associated with gray level(s) of a contrastagent is provided. The process 1000 may include the followingoperations:

In 10602, one or more gradient images corresponding to the one or moreventricular images may be determined.

In some embodiments, the processing device 140 (e.g., the cardiac motionparameter determination module 17300) may perform operation 10602.

Specifically, in some embodiments, gradient image(s) of the ventricularimage(s) may be determined based on the ventricular image(s). In imageprocessing, modulus (or moduli) of gradient(s) may be simply referred asgradient(s), and an image using the gradient(s) as elements may bereferred as a gradient image. If an image (e.g., a ventricular image)includes an edge (e.g., of different portions of an object), acorresponding gradient image may include relatively large gradientvalue(s). If the image includes a relatively smooth part, anddifference(s) between gray level(s) are relatively low, then thecorresponding gradient value(s) are relatively low. In some embodiments,the determination of the gradient image(s) may be performed using theSobel operator. The Sobel operator is a discrete first-order differenceoperator used to determine an approximation of a first-order gradient ofan image brightness function. A gradient vector corresponding to anelement of an image may be generated by applying the Sobel operator tothe element in the image.

In an embodiment, the gradient image(s) may be determined as:

$\begin{matrix}{{HeartImg} = \left\{ {\begin{matrix}{OriImg} & {{{if}{MaskImg}} = 1} \\0 & {esle}\end{matrix},} \right.} & {{Equation}(11)}\end{matrix}$ $\begin{matrix}{{{GradImg} = \frac{\partial{HeartImg}}{\partial\left( {x,y} \right)}},} & {{Equation}(12)}\end{matrix}$

where GradImg refers to the gray levels of elements of a gradient image,HeartImg is a ventricular image, and (x, y) is a gray level of anelement with an abscissa value x and an ordinate value y.

In Equations (11) and (12), MaskImg refers to the pleural image, OriImgrefers to the set of initial cardiac images of interest.

In 10604, a target ventricular image whose corresponding gradient imagehas elements with gray levels larger than a proportional threshold maybe determined from the one or more ventricular images as a marker image.

In some embodiments, the processing device 140 (e.g., the cardiac motionparameter determination module 17300) may perform operation 10604.

Specifically, in some embodiments, the gray level(s) of the element(s)in the gradient image may be analyzed statistically, and a targetventricular image whose corresponding gradient image has elements withvalues larger than a proportional threshold may be determined as amarker image. In some embodiments, the gray level(s) of the element(s)in the gradient image may be analyzed statistically to obtain ahistogram of the element(s); an appropriate proportion of gray level(s)may be selected as a proportional threshold; and the element(s) withgray level(s) greater than the proportional threshold may be extractedto obtain a marker image.

In an embodiment, the marker image may be determined as:

$\begin{matrix}{{MarkerImg} = \left\{ {\begin{matrix}{HeartImg} & {{GradImg} > Q} \\0 & {{GradImg} \leq Q}\end{matrix},} \right.} & {{Equation}(13)}\end{matrix}$

where MarkerImg is a marker image, GradImg refers to gray levels of agradient image, and HeartImg is a ventricular image.

In Equation (13), Q is the proportional threshold. In some embodiments,Q may be in a range from 0 to 1.

In 10606, the first threshold associated with the gray level(s) of thecontrast agent may be determined based on the marker image and/or anOTSU algorithm.

In some embodiments, the processing device 140 (e.g., the cardiac motionparameter determination module 17300) may perform operation 10606. Insome embodiments, the first threshold may refer to a thresholdassociated with the contrast agent.

Specifically, in some embodiments, the threshold associated with thecontrast agent may be determined based on the value(s) of the element(s)of the marker image using the OTSU algorithm. The OTSU algorithm is anefficient algorithm for the binarization of image(s), using a thresholdto segment an original image into a foreground image and a backgroundimage. An optimal segment threshold may be taken as the thresholdassociated with the gray level(s) of the contrast agent.

In an embodiment, the threshold associated with the contrast agent maybe determined as:

ContrastThre=Otsuthresh(MarkerImg),  Equation (14)

where ContrastThre is threshold associated with the contrast agent,MarkerImg is a marker image, and Otsuthresh refers to the OTSUalgorithm.

According to the process for determining the threshold associated withthe contrast agent, the gradient image(s) of the ventricular image(s)may be determined; a target ventricular image whose correspondinggradient image has elements with gray levels larger than theproportional threshold may be determined as a marker image; and thethreshold associated with the contrast agent may be determined based onthe values of the elements of the marker image using the OTSU algorithm.Therefore, threshold(s) associated with contrast agent(s) at differentconcentrations may be determined, thereby improving the accuracy of thedetermination of the contrast agent image(s) by segmenting theventricular image(s) based on the determined threshold associated withthe contrast agent.

In an embodiment, a process for obtaining vascular image(s) of interestbased on ventricular image(s) and the threshold associated with thecontrast agent is provided. The process may include the followingoperations:

In 10608, one or more contrast agent images may be obtained bysegmenting the one or more ventricular images based on the firstthreshold.

In some embodiments, the processing device 140 (e.g., the cardiac motionparameter determination module 17300) may perform operation 10608.

In an embodiment, the obtaining of the contrast agent image(s) bysegmenting the ventricular image(s) based on the threshold associatedwith the contrast agent may be represented as:

$\begin{matrix}{{ContrastImg} = \left\{ {\begin{matrix}{OriImg} & {{HeartImg} > {ContrstThre}} \\0 & {{HeartImg} \leq {ContrstThre}}\end{matrix},} \right.} & {{Equation}(15)}\end{matrix}$

where ContrastImg refers to the contrast agent image(s), HeartImg refersto ventricular image(s), and ContrstThre refers to the thresholdassociated with the contrast agent.

In Equation (15), OriImg refers to the set of initial cardiac images ofinterest.

In 10610, one or more vascular images of interest may be extracted basedon the one or more contrast agent images.

In some embodiments, the processing device 140 (e.g., the cardiac motionparameter determination module 17300) may perform operation 10610.

In an embodiment, an image of the right coronary artery of the heart maybe extracted as a vascular image of interest. The right coronary arteryis an arterial blood vessel that is clinically more visible than otherblood vessels, the motion of the right coronary artery may reflect themotion of the heart, and then the motion of the heart in differentphases may be determined by detecting the motion of the right coronaryartery in the corresponding phases. The right coronary artery maygenerally have the following characteristics: the position of the rightcoronary artery may be located in the upper left part of the ventricle;the number (or count) of elements associated with the contrast agent maybe relatively low; the CT value(s) of extravascular element(s) may berelatively low. Connected domain(s) associated with the contrast agentthat have the characteristics mentioned above may be extracted asvascular image(s) of interest.

FIG. 11 is a flowchart illustrating an exemplary process for determiningcardiac motion parameter(s) according to some embodiments of the presentdisclosure. In some embodiments, operation 7306 illustrated in FIG. 7may be performed according to the process 1100.

In an embodiment, as shown in FIG. 11 , an exemplary process fordetermining motion rate(s) of vascular center(s) is provided. Theprocess 1100 may include the following operations:

In 11702, vascular centers may be determined in the vascular image(s) ofinterest.

In some embodiments, the processing device 140 (e.g., the cardiac motionparameter determination module 17300) may perform operation 11702. Insome embodiments, a first vascular center may be determined in eachvascular image of interest of each sampled cardiac motion phase.

Specifically, in some embodiments, vascular center(s) may be selected inthe vascular image(s) of interest.

In some embodiments, a vascular center may be determined in eachvascular image of interest of each sampled cardiac motion phase.

In an embodiment, the vascular center(s) may be determined as:

(x _(p) _(n) ,y _(p) _(n) )=Center(VesselImg_(p) _(n) ),  Equation (16)

where VessalImg refers to a vascular image of interest, x_(p) _(n)refers to an abscissa value of a vascular center, y_(p) _(n) refers toan ordinate value of the vascular center, p refers to a sampled cardiacmotion phase, and n refers to the sequence number of the sampled cardiacmotion phase. In some embodiments, n may be 1, 2, . . . , or 10.Correspondingly, in some embodiments, p may be 10%, 20%, . . . , or100%.

In 11704, the displacement(s) of the vascular center(s) between multiplesampled cardiac motion phases may be determined based on vascularcenter(s) of the sampled cardiac motion phase(s).

In some embodiments, the processing device 140 (e.g., the cardiac motionparameter determination module 17300) may perform operation 11704. Insome embodiments, a displacement of the first vascular center may bedetermined based on a first position of the first vascular center in theeach vascular image of interest and/or a second position of a secondvascular center in a vascular image of interest of another sampledcardiac motion phase adjacent to the each sampled cardiac motion phase.

Specifically, in some embodiments, the displacement(s) of the vascularcenter(s) between multiple sampled cardiac motion phases may bedetermined based on vascular center(s) of adjacent sampled cardiacmotion phases.

In an embodiment, the displacement(s) of the vascular center(s) may bedetermined as:

Displacement_(p) _(n) =√{square root over ((x _(p) _(n) −x _(p) _(n-1))²+(y _(p) _(n) −y _(p) _(n-1) )²)},  Equation (17)

where Displacement refers to a displacement of a vascular center, x_(p)_(n) refers to an abscissa value of a vascular center, y_(p) _(n) refersto an ordinate value of the vascular center, p refers to a sampledcardiac motion phase, and n refers to the sequence number of the sampledcardiac motion phase. In some embodiments, n may be 1, 2, . . . , or 10.Correspondingly, in some embodiments, p may be 10%, 20%, . . . , or100%.

In 11706, sampling interval(s) between the multiple sampled cardiacmotion phases.

In some embodiments, the processing device 140 (e.g., the cardiac motionparameter determination module 17300) may perform operation 11706. Insome embodiments, a sampling interval between the each sampled cardiacmotion phase and the another sampled cardiac motion phase adjacent tothe each sampled cardiac motion phase may be determined.

Specifically, in some embodiments, the beat frequency of the heart maybe variable. An equal sampling interval may refer that samplingintervals in each cardiac cycle are equal. The sampling interval(s)between different sampled cardiac motion phases may be different.Therefore, it is desirable to determine the sampling interval(s) basedon instantaneous time points in each cardiac cycle and a referencecardiac cycle time.

In an embodiment, the sampling interval(s) may be determined as:

$\begin{matrix}{{{RR}_{frac} = {{{delay}\left( {{RR}_{inst},p^{n}} \right)} - {{delay}\left( {{RR}_{inst},p^{n - 1}} \right)}}},} & {{Equation}(18)}\end{matrix}$ $\begin{matrix}{{{{delay}\left( {{RR}_{inst},p^{n}} \right)} = {{RR_{inst} \times \frac{P{D\left( p^{n} \right)}}{100}} + {{DO}\left( P_{p} \right)}}},} & {{Equation}(19)}\end{matrix}$ $\begin{matrix}{{{PD} = {{round}\left( {p^{n} \times {C\left( p^{n} \right)}} \right)}},} & {{Equation}(20)}\end{matrix}$ $\begin{matrix}{{{DO} = {{round}\left\lbrack {{RR}_{ref} \times \left( {p^{n}/100} \right) \times \left( {1 - {C\left( p^{n} \right)}} \right)} \right\rbrack}},} & {{Equation}(21)}\end{matrix}$ $\begin{matrix}{{{C\left( p^{n} \right)} = {1 - \left( {1 - {p^{n}/100}} \right)^{2}}},} & {{Equation}(22)}\end{matrix}$

where RR_(frac) refers to a sampling interval, RR_(inst) refers to aninstantaneous time in a cardiac cycle, RR_(ref) refers to a referencecardiac cycle time, and p refers to a sampled cardiac motion phase.

In Equations (18)-(22), n refers to the sequence number of the sampledcardiac motion phase, delay refers to a time delay of a phase p relativeto the instantaneous time in a cardiac cycle (in ms), PD refers to thepercentage of the R-R interval, DO refers to a fixed delay offset (inms) from the percentage location within the R-R interval, round refersto the round function, C refers to the compliance curve function.

In 11708, quotient(s) of the displacement(s) of the vascular center(s)between the sampled cardiac motion phase(s) divided by correspondingsampling interval(s) may be determined, and the quotient(s) may bedetermined as motion rate(s) of the vascular center(s).

In some embodiments, the processing device 140 (e.g., the cardiac motionparameter determination module 17300) may perform operation 11708. Insome embodiments, a motion rate of a vascular center may be determinedbased on a displacement of the vascular center between two sampledcardiac motion phases and the corresponding sampling interval betweenthe two sampled cardiac motion phases.

In an embodiment, the motion rate(s) of the vascular center(s) may bedetermined as:

Velocity_(n)=Displacement_(n) /RR _(frac),  Equation (23)

where Displacement refers to a displacement of a vascular center,RR_(frac) refers to a corresponding sampling interval, Velocity refersto a motion rate of the vascular center, and n refers to the sequencenumber of the sampled cardiac motion phase(s).

In 11710, cardiac motion parameter(s) corresponding to the sampledcardiac motion phase(s) may be determined based on the motion rate(s) ofthe vascular center(s) of the sampled cardiac motion phase(s).

In some embodiments, the processing device 140 (e.g., the cardiac motionparameter determination module 17300) may perform operation 11710. Insome embodiments, a motion rate of the first vascular center may bedetermined based on the displacement and the corresponding samplinginterval. In some embodiments, the motion rate of the first vascularcenter may be designated as a cardiac motion parameter.

Specifically, in some embodiments, a mean phase may be selected from theplurality of (sampled) cardiac motion phases according to the cardiacmotion parameter(s) of the plurality of (sampled) cardiac motion phases.Alternatively or additionally, other cardiac motion parameter(s) ofother phase(s) excluding the plurality of (sampled) cardiac motionphases may be obtained by performing an interpolation operation on thecardiac motion parameter(s) of the plurality of (sampled) cardiac motionphases, and a mean phase may be selected from the other phase(s).

According to the process for determining motion rate(s) of the vascularcenter(s), the vascular center(s) may be determined in the vascularimage(s) of interest; displacement(s) of the vascular center(s) betweenthe sampled cardiac motion phases may be determined according to thevascular center(s) of the sampled cardiac motion phases; samplinginterval(s) between the sampled cardiac motion phases may be determined;and quotient(s) of the displacement(s) of the vascular center(s) betweenthe sampled cardiac motion phases divided by corresponding samplinginterval(s) may be determined to obtain the motion rate(s) of thevascular center(s). Therefore, the accuracy of the determination ofmotion parameter(s) of the vascular center(s) between adjacent sampledcardiac motion phase(s) may be improved, and accordingly, a sampledcardiac motion phase with a minimum motion amplitude or intensity may beselected accurately.

FIG. 12 is a flowchart illustrating an exemplary process for determininga mean phase based on motion rate(s) of vascular center(s) according tosome embodiments of the present disclosure. In some embodiments,operation 5108 illustrated in FIG. 5 may be performed according to theprocess 1200.

In an embodiment, as shown in FIG. 12 , an exemplary process fordetermining a mean phase based on the motion rate(s) of the vascularcenter(s) is provided. The process 1200 may include the followingoperations:

In 12802, weighting operation(s) may be performed on the motion rate(s)of the vascular center(s) of the sampled cardiac motion phase(s) and aratio of the sampled cardiac motion phases to all the cardiac motionphases, to obtain weighted motion rate(s) between the sampled cardiacmotion phase(s).

In some embodiments, the processing device 140 (e.g., the mean phasedetermination module 17400) may perform operation 12802.

Specifically, in some embodiments, if the weighted motion rate(s) arerelatively large, the motion during the corresponding samplinginterval(s) may be relatively stable.

In an embodiment, the weighted motion rate(s) may be determined as:

$\begin{matrix}{{{Velocity}_{n}^{w} = {{weight}_{n}*\left( \frac{{\max\left( {Velocity}_{n} \right)} - {Velocity}_{n}}{{\max\left( {Velocity}_{n} \right)} - {\min\left( {Velocity}_{n} \right)}} \right)}},} & {{Equation}(24)}\end{matrix}$

where Velocity_(n) ^(w) refers to a weighted motion rate, weight refersto the ratio of the sampled cardiac motion phases to all the cardiacmotion phases, and Velocity refers to a motion rate of a vascularcenter.

In Equation (24), n refers to the sequence number of the sampled cardiacmotion phase.

In 12804, the cardiac motion parameters of all the cardiac motion phasesmay be obtained by performing an interpolation on the weighted motionrate(s).

In some embodiments, the processing device 140 (e.g., the mean phasedetermination module 17400) may perform operation 12804.

Specifically, in some embodiments, the “interpolation operation” may useknown function values of one or more points in a certain section todetermine an appropriate specific function. Function values of otherpoints between the certain section may be used as an approximation ofthe specific function. This operation may be regarded as interpolation.

In an embodiment, the cardiac motion parameter(s) may be determined as:

V=interp1(Velocity_(n) ^(w) ,x,xi,‘spline’),  Equation (25)

x=(p ^(n) −p ^(n-1))/2,xi=[x(1): 1: x(end)],  Equation (26)

where V refers to the motion parameter(s) after interpolation, interp1refers to a one-dimensional interpolation operation, n refers to thesequence number of the sampled cardiac motion phase(s), w refers toweight(s), x refers to sampled cardiac motion phase(s), xi refers tosampled cardiac motion phase(s) after interpolation, and ‘spline’ refersthat the interpolation operation is a spline interpolation.

In Equations (25)-(26), p^(n) refers to the nth sampled cardiac motionphase, x(1) refers to the first sampled cardiac motion phase beforeinterpolation, and x(end) refers to the last sampled cardiac motionphase before interpolation. In some embodiments, x(1) may be (p²−p¹)/2,and x(end) may be (p^(LN)−p^(LN-1))/2, wherein LN refers to the lastsampled cardiac motion phase before interpolation.

In 12806, a cardiac motion phase corresponding to a maximum motionparameter may be designated as the mean phase.

In some embodiments, the processing device 140 (e.g., the mean phasedetermination module 17400) may perform operation 12806. In someembodiments, the motion parameter herein may be regarded as a secondmotion parameter, which may be different from the first motion parameterdescribed in FIG. 6 . For example, if the second motion parameter isrelatively large, or the first cardiac motion parameter is relativelysmall, the cardiac motion may be relatively smooth.

Specifically, in some embodiments, according to the determined cardiacmotion parameters of all the cardiac motion phases, a cardiac motionphase with a maximum motion parameter may be selected from the cardiacmotion parameters of all the cardiac motion phases as the mean phase.

According to the process for determining the mean phase, weighted motionrate(s) between the sampled cardiac motion phase(s) may be obtained;cardiac motion parameters of all the cardiac motion phases may beobtained by performing an interpolation (e.g., a second-order derivablespline interpolation) on the weighted motion rate(s); and a cardiacmotion phase with a maximum motion parameter may be selected as the meanphase. Therefore, a phase corresponding to a mild cardiac motion may beselected, according to the motion of the heart, as the mean phase.

In some embodiments, if the phase corresponding to the maximum cardiacmotion parameter is selected as the mean phase, the cardiac motioncorresponding to the mean phase may be relatively smooth (or mild),which can reduce the artifact(s) in the reconstructed cardiac image(s)and accordingly improve the quality of the reconstructed cardiacimage(s).

FIG. 13 is a flowchart illustrating an exemplary process for obtaining aset of target cardiac images according to some embodiments of thepresent disclosure. In some embodiments, operation 5114 illustrated inFIG. 5 may be performed according to operations 13904 and 13906 of theprocess 1300.

In an embodiment, as shown in FIG. 13 , an exemplary process forobtaining a set of cardiac images corresponding to optimal phase(s) isprovided. The process 1300 may include the following operations:

In 13902, one or more cardiac motion phases in a preset range includingthe mean phase may be selected in each cardiac cycle of a plurality ofcardiac cycles; and one or more cardiac images of the one or morecardiac motion phases may be reconstructed.

In some embodiments, the processing device 140 (e.g., the secondreconstruction module 17500) may perform operation 13902. In someembodiments, the cardiac image(s) of the cardiac motion phase(s) may bereconstructed based on one or more sub-sets of projection datacorresponding to the one or more cardiac motion phases in the eachcardiac cycle.

Specifically, in some embodiments, heart motions of different patientsmay be different (or heart motion of a same patient in different cardiaccycles may be different), and the mean phase of a patient for differentcardiac cycles within a period of time may be inconsistent. Therefore,it may be desirable to further obtain a phase of interest in eachcardiac cycle for the patient based on the mean phase. On the basis ofthe mean phase, projection data of cardiac motion phase(s) within apreset range including the mean phase in each cardiac cycle may beselected, and corresponding cardiac image(s) may be reconstructedaccording to the projection data. In some embodiments, the preset rangemay be 2%, 5%, 10%, or the like. That is, phases in a range of 10%around the mean phase may be selected in each cardiac cycle, and cardiacimage(s) corresponding to the selected phases may be reconstructed.

In 13904, a phase of interest may be determined in the each cardiaccycle based on one or more cardiac images of the each cardiac cycle.

In some embodiments, the processing device 140 (e.g., the secondreconstruction module 17500) may perform operation 13904. In someembodiments, the optimal phase in the each cardiac cycle may refer tothe phase of interest in the each cardiac cycle. In some embodiments,the cardiac image(s) corresponding to the optimal phase(s) may bereferred to as the set of target cardiac images. In some embodiments, atarget cardiac image may be referred to as a cardiac image of interest.

Specifically, in some embodiments, image(s) of a region of interest maybe determined according to the cardiac image(s) corresponding to thephase within the preset range. A top-hat transformation may be performedon the image(s) of the region of interest to obtain a transformed imageof the region of interest. A maximum gray level of the transformed imageof the region of interest may be selected. The maximum gray levelmultiplied by one or more preset values may be designated as segmentthreshold(s). Elements that have gray levels greater than a segmentthreshold may be extracted from the transformed image of interest as asegmentation image corresponding to the segment threshold. Thus one ormore segmentation images associated with each image of the region ofinterest may be obtained based on the one or more segment thresholds. Aperimeter and an area of a blood vessel in each segmentation image ofthe segmentation images may be further determined. A compactness degreeof the each segmentation image may be determined according to theperimeter and the area of a target object (e.g., the blood vessel) inthe each segmentation image. A regularity degree of an image of theregion of interest may be determined based on the compactness degree(s)of the segmentation image(s) associated with the image of the region ofinterest. A cardiac motion phase corresponding to an image of the regionof interest that has a maximum regularity degree may be designated asthe optimal phase of the each cardiac cycle.

In 13906, a set of target cardiac images may be obtained by determininga target cardiac image of the phase of interest in the each cardiaccycle.

In some embodiments, the processing device 140 (e.g., the secondreconstruction module 17500) may perform operation 13906.

Specifically, in some embodiments, the obtaining of the set of targetcardiac images may include: reconstructing based on projection data of aphase of interest in the each cardiac cycle to obtain the set of targetcardiac images; or selecting a target cardiac image of the each cardiaccycle from a plurality of reconstructed cardiac images based on thephase of interest in the each cardiac cycle to obtain the set of targetcardiac images.

According to the process for obtaining a set of target cardiac images,cardiac motion phases in a preset range including the mean phase in eachcardiac cycle may be selected; a phase of interest may be determined inthe each cardiac cycle based on the cardiac image(s) of the each cardiaccycle; and a set of target cardiac images may be obtained. Therefore, anoptimal phase of each cardiac cycle may be accurately determined,thereby reducing the artifacts caused by cardiac motion, and furtherimproving the image quality of the target cardiac image(s).

FIG. 14 is a flowchart illustrating an exemplary process for determininga phase of interest in a cardiac cycle according to some embodiments ofthe present disclosure. In some embodiments, operations 5112 and/or 5114illustrated in FIG. 5 may be performed according to the process 1400.

In an embodiment, as shown in FIG. 14 , an exemplary process fordetermining an optimal phase for each cardiac cycle is provided. Theprocess 1400 may include the following operations:

In 141001, one or more images of a region of interest may be determinedbased on cardiac images of the cardiac motion phases in the preset rangeincluding the mean phase.

In some embodiments, the processing device 140 (e.g., the secondreconstruction module 17500) may perform operation 141001. In someembodiments, an image of a region of interest may be obtained byextracting the region of interest in one (e.g., each) of the cardiacimages of the cardiac motion phases in the preset range in a cardiaccycle.

Specifically, in some embodiments, for the cardiac images of the cardiacmotion phases within the preset range, the backgrounds of the cardiacimages may be mostly the same, and thus redundant information in thecardiac images of the cardiac motion phases within the preset range maybe excessive. Therefore, it is desirable to extract a specific region ofinterest (e.g., a blood vessel) in the cardiac images for furtheranalysis. In one (e.g., each) of the cardiac images of the cardiacmotion phases within the preset range, a center of the blood vessel maybe selected as a center point, and then a neighborhood matrix R×R aroundthe center point may be selected as elements of an image of the regionof interest.

In an embodiment, the image of the region of interest may be determinedas:

$\begin{matrix}{{{Iroi} = {{Iori}\left( {{\frac{\left( {R - 1} \right)}{2} - {X^{cen}:X^{cen}} + \frac{\left( {R - 1} \right)}{2}},{\frac{\left( {R - 1} \right)}{2} - {Y^{cen}:Y^{cen}} + \frac{\left( {R - 1} \right)}{2}}} \right)}},} & {{Equation}(27)}\end{matrix}$

where Iroi refers to the image(s) of the region of interest, X^(cen)refers to the abscissa value(s) of point(s) in a centerline of the bloodvessel in the cardiac images (or centers of the blood vessel in thecardiac images), Y^(cen) refers to the ordinate value of point(s) in thecenterline of the blood vessel in the cardiac images (or centers of theblood vessel in the cardiac images), and R refers to a size of theneighborhood matrix.

In some embodiments, R may be a default value determined by the imagingsystem 100 or preset by a user or operator via the terminal(s) 130. Insome embodiments, the centerline of the blood vessel may include thevascular center(s) illustrated in operation 11702 of FIG. 11 .

In 141002, a maximum gray level (of an image of the region of interest)multiplied by one or more preset values may be determined as segmentthreshold(s).

In some embodiments, the processing device 140 (e.g., the secondreconstruction module 17500) may perform operation 141002. In someembodiments, at least one threshold associated with a maximum gray levelof the image of the region of interest corresponding to the cardiacimage may be determined. The at least one threshold may be referred toas the segment threshold(s).

Specifically, in some embodiments, a top-hat transformation may beperformed on the image of the region of interest to obtain a transformedimage of the region of interest. The maximum gray level of thetransformed image of the region of interest may be selected. The maximumgray level multiplied by the one or more preset values may be designatedas the segment threshold(s).

In some embodiments, the transformed image of the region of interest maymainly include information of a target object (e.g., a blood vessel).More descriptions of the determination of the segment threshold(s) maybe found elsewhere in the present disclosure (e.g., FIG. 15 anddescriptions thereof).

In 141003, at least one vascular image of interest may be obtained basedon the image of the region of interest corresponding to the cardiacimage and the segment threshold(s).

In some embodiments, the processing device 140 (e.g., the secondreconstruction module 17500) may perform operation 141003. In someembodiments, the vascular image(s) of interest may be obtained bysegmenting the image of the region of interest corresponding to thecardiac image based on the at least one threshold (i.e., the segmentthreshold(s)). In some embodiments, the vascular image of interest mayinclude a vascular vessle (e.g., the right coronary artery). Moredescriptions of the at least one vascular image of interest may be foundelsewhere in the present disclosure (e.g., FIGS. 7, 8, and 10 , anddescriptions thereof). In some embodiments, two or more segmentationoperations may be performed based on two or more different thresholds toobtain two or more vascular images of interest. The vascular vessels inthe two or more vascular images of interest may have differentmorphologies. For example, a vascular vessel in a vascular image ofinterest obtained based on a relatively large threshold may have aregular morphology. As another example, a vascular vessel in a vascularimage of interest obtained based on a relatively low threshold may havemore details and/or artifacts near the edge of the vascular vessel, anda relatively low regularity. Therefore, the two or more vascular imagesof interest may include comprehensive information relating to thevascular vessels.

Specifically, in some embodiments, elements of the transformed image ofthe region of interest that have gray levels greater than a segmentthreshold may be extracted as a segmentation image corresponding to thesegment threshold. A segmentation operation may be performed on thetransformed image of the region of interest generated by the top-hattransformation based on a segment threshold, and a segmentation imagecorresponding to the segment threshold may be obtained. Multiplesegmentation images may be obtained by using multiple segment thresholdsin segmentation operation(s). Merely by way of example, the transformedimage of the region of interest may be segmented based on a firstsegment threshold, and elements of the transformed image of the regionof interest that have gray levels greater than the first segmentthreshold may be extracted as a first segmentation image; thetransformed image of the region of interest may be segmented based on asecond segment threshold, and elements of the transformed image of theregion of interest that have gray levels greater than the second segmentthreshold may be extracted as a second segmentation image; thetransformed image of the region of interest may be segmented based on athird segment threshold, and elements of the transformed image of theregion of interest that have gray levels greater than the third segmentthreshold may be extracted as a third segmentation image; thetransformed image of the region of interest may be segmented based on afourth segment threshold, and elements of the transformed image of theregion of interest that have gray levels greater than the fourth segmentthreshold may be extracted as a fourth segmentation image.

In some embodiments, a segmentation image may also be referred to as avascular image of interest.

In 141004, a regularity degree of the image of the region of interestcorresponding to the cardiac image may be determined based on the atleast one vascular image of interest.

In some embodiments, the processing device 140 (e.g., the secondreconstruction module 17500) may perform operation 141004. In someembodiments, a regularity degree of the image of the region of interestcorresponding to each cardiac image may be determined based on the atleast one vascular image of interest.

Specifically, in some embodiments, a perimeter and an area of a targetobject may be determined in each segmentation image according to aplurality of segmentation images; a compactness degree of the eachsegmentation image may be determined according to the perimeter and thearea of the target object in the each segmentation image; and aregularity degree of the image of the region of interest may bedetermined according to the compactness degree of each segmentationimage corresponding to the image of the region of interest.

More descriptions of the determination of the regularity degree(s) maybe found elsewhere in the present disclosure (e.g., FIG. 16 anddescriptions thereof).

In 141005, a target image of the region of interest with a maximumregularity degree may be determined among a plurality of images of theregion of interest; and a phase of the target image of the region ofinterest may be designated as the phase of interest.

In some embodiments, the processing device 140 (e.g., the secondreconstruction module 17500) may perform operation 141005. In someembodiments, the phase of interest may refer to an optimal phase in aspecific cardiac cycle.

Specifically, in some embodiments, a regularity degree may be determinedfor each image of the region of interest in the preset range of acardiac cycle; and all regularity degrees of the images of the region ofinterest in the preset range may be compared to determine a maximumregularity degree. A phase of the target image of the region of interestwith the maximum regularity degree may be designated as an optimal phaseof the cardiac cycle. Then projection data corresponding to the optimalphase for the cardiac cycle may be selected.

According to the process for determining an optimal phase data of acardiac cycle, image(s) of a region of interest may be determined basedon cardiac image(s) of the cardiac motion phases within the presetrange; a top-hat transformation may be performed to obtain a transformedimage of the region of interest; a maximum gray level of the elements inan image of the region of interest may be determined; one or moresegment thresholds may be determined according to the maximum graylevel; the image of the region of interest may be segmented according tothe segment threshold(s) to obtain one or more segmentation images; aperimeter and an area of a target object in each segmentation image maybe determined; and a compactness degree of the segmentation image may bedetermined based on the perimeter and the area of the segmentationimage. A regularity degree of an image of the region of interest may bedetermined based on one or more compactness degrees of the one or moresegmentation images of the image of the region of interest; and a phaseof a target image of the region of interest that has a maximumregularity degree may be designated as an optimal phase of a cardiaccycle. According to the regularity degree(s), the optimal phase ofcardiac motion may be determined more efficiently, and the cardiac imageof the optimal phase may be determined based on the optimal phaseconveniently.

FIG. 15 is a flowchart illustrating an exemplary process for determiningsegment threshold(s) according to some embodiments of the presentdisclosure. In some embodiments, operation 141002 illustrated in FIG. 14may be performed according to the process 1500.

In an embodiment, as shown in FIG. 15 , an exemplary process fordetermining segment threshold(s) is provided. The process 1500 mayinclude the following operations:

In 151101, a transformed image of the region of interest may be obtainedby performing a top-hat transformation on the image of the region ofinterest.

In some embodiments, the processing device 140 (e.g., the secondreconstruction module 17500) may perform operation 151101. In someembodiments, the top-hat transformation may be performed to enchance theimage contrast of the image of the region of interest, facilitating thefurther processing of the image of the region of interest (e.g., makingthe threshold(s) more suitable for the segmentation of the images of theregion of interest).

Specifically, in some embodiments, the top-hat transformation is animage processing algorithm that may weaken a background in an image andmake a target object more prominent. That is, the top-hat transformationof the image of the region of interest may make a target object in theimage of the region of interest more prominent. In some embodiments, thetarget object may include a blood vessel. After the top-hattransformation of the image of the region of interest, the background(in the image of the region of interest) may be weakened and the bloodvessel(s) (in the image of the region of interest) may be shown moreclearly.

In 151102, a maximum gray level of the transformed image of the regionof interest may be determined.

In some embodiments, the processing device 140 (e.g., the secondreconstruction module 17500) may perform operation 151102.

Specifically, in some embodiments, the gray levels of all the elementsof the transformed image of the region of interest may be extracted, andthe gray levels of all the elements may be compared to obtain a maximumgray level of the gray levels.

In 151103, the maximum gray level multiplied by at least one presetvalue may be designated as the at least one threshold.

In some embodiments, the processing device 140 (e.g., the secondreconstruction module 17500) may perform operation 151103.

Specifically, in some embodiments, the maximum gray level multiplied bya preset value may be designated as a segment threshold, and a pluralityof segment thresholds may be obtained based on a plurality of presetvalues. The preset value(s) may be a number between 0 and 1. In someembodiments, multiple segment thresholds may be determined based onmultiple preset values, which can facilitate the segmentation of bloodvessel(s) that affect (or induce) motion artifacts.

According to the process for determining the segment threshold(s),segment threshold(s) may be determined according to the maximum graylevel of the (transformed) image of the region of interest and thepreset value(s); and segmentation image(s) may be determined bysegmenting the (transformed) image of the region of interest accordingto the segment threshold(s). Therefore, the maximum gray level may beaccurately determined, and the (transformed) image of the region ofinterest may be segmented more precisely.

FIG. 16 is a flowchart illustrating an exemplary process for determiningregularity degree(s) according to some embodiments of the presentdisclosure. In some embodiments, operation 141004 illustrated in FIG. 14may be performed according to the process 1600.

In an embodiment, as shown in FIG. 16 , an exemplary process fordetermining regularity degree(s) is provided. The process 1600 mayinclude the following operations:

In 161201, a perimeter and an area of a target object in a vascularimage of interest may be determined.

In some embodiments, the processing device 140 (e.g., the secondreconstruction module 17500) may perform operation 161201. In someembodiments, a perimeter and an area of a target object in each vascularimage of interest may be determined.

Specifically, in some embodiments, according to the obtainedsegmentation image(s), a perimeter and an area of a target object (e.g.,a blood vessel) in one or more (e.g., each) segmentation images may berespectively determined. In some embodiments, the perimeter and the areaof the blood vessel in each segmentation image are respectivelydetermined.

In some embodiments, a segmentation image may also be referred to as avascular image of interest.

In 161202, a compactness degree of the vascular image of interest may bedetermined based on the perimeter and the area of the target object.

In some embodiments, the processing device 140 (e.g., the secondreconstruction module 17500) may perform operation 161202. In someembodiments, the compactness degree may reflect a closeness degree ofthe element(s) in the vascular image of interest or a region of interestthereof (e.g., the elements corresponding to the target object). Thecompactness degree may relate to a perimeter and/or an area of a region(e.g., the target object) including a portion or all elements in thevascular image of interest or a region of interest thereof. In someembodiments, the compactness degree may be in direct proportion to theperimeter and/or inversely proportional to the area. For example, thecompactness degree of a circle may be 1. In some embodiments, thecompactness degree of the vascular image of interest may be determinedbased on the perimeter and the area of the target object.

Specifically, in some embodiments, the compactness degree may bedetermined as:

$\begin{matrix}{{{{Compatnes}s_{i}} = {{Li}/\left( 2{\pi \times \sqrt{\frac{Si}{\pi}}} \right)}},} & {{Equation}(28)}\end{matrix}$

where Compatness refers to the compactness degree, Li refers to theperimeter of the target object in an i-th segmentation image, Si refersto the area of the target object in the i-th segmentation image.

In 161203, a regularity degree of the image of the region of interestcorresponding to the cardiac image may be determined based on at leastone compactness degree of the at least one vascular image of interest.

In some embodiments, the processing device 140 (e.g., the secondreconstruction module 17500) may perform operation 161203. In someembodiments, the regularity degree may reflect an orderliness of theelement(s) in the image of the region of interest. For example, theorderliness of a polygon may be lower than a circle, and accordingly,the regularity degree of the polygon may be lower than the circle. Asanother example, if an image has a relatively high level of artifact(s),i.e., the clarity of the boundary (or boundaries) of different regionsin the image is relatively low, then the regularity degree of the imagemay be relatively low. In some embodiments, if an image has a relativelylarge regularity degree, the image may have a relatively high clarity(or quality) and/or a low level of artifacts. In some embodiments, aregularity degree of the image of the region of interest correspondingto the cardiac image may be determined based on the compactnessdegree(s) of the vascular image(s) of interest (e.g., the at least onevascular image of interest described in operations 141003 and 141004).

Specifically, in some embodiment, the regularity degree may bedetermined as:

ConIndex=Σ_(i) ^(N) N×(2−min(Compatness_(i),2))/ΣN,  Equation (29)

where ConIndex refers to the regularity degree, Compatness refers to thecompactness degree, i refers to an ith segmentation image, and N refersto the number of preset values.

In some embodiments, the number of preset values may equal to the numberof the segmentation images corresponding to the image of the region ofinterest.

According to the process for determining the regularity degree, theregularity degree of the image of the region of interest may bedetermined accurately, the optimal phase of cardiac motion may bedetermined accurately, and thus, a cardiac image of the optimal phasemay be obtained based on the optimal phase.

According to the process for determining the optimal phase, image(s) ofa region of interest may be determined based on cardiac image(s) of thecardiac motion phases within the preset range; a top-hat transformationmay be performed to obtain a transformed image of the region ofinterest; a maximum gray level of the elements in an image of the regionof interest may be determined; one or more segment thresholds may bedetermined according to the maximum gray level; the image of the regionof interest may be segmented according to the segment threshold(s) toobtain one or more segmentation images; a perimeter and an area of atarget object in each segmentation image may be determined; and acompactness degree of the segmentation image may be determined based onthe perimeter and the area of the segmentation image. A regularitydegree of an image of the region of interest may be determined based onone or more compactness degrees of the one or more segmentation imagesof the image of the region of interest; and a phase of a target image ofthe region of interest that has a maximum regularity degree may bedesignated as an optimal phase of a cardiac cycle. According to theregularity degree(s), the optimal phase of cardiac motion may bedetermined more efficiently, and the cardiac image of the optimal phasemay be determined based on the optimal phase conveniently.

Compared with traditional process(es) for determining the optimal phase,the cardiac image reconstruction process(es) illustrated above may havea higher accuracy, be independent of artificial selection of the regionof interest, and may actively identify the vascular image of interest.And in the traditional process(es) for determining the optimal phase, asame optimal phase may be selected for all cardiac cycles. The optimalphase may not be most accurate for each cardiac cycle. Using theprocess(es) illustrated above, the optimal phase of each cardiac cyclemay be determined separately. For patient(s) with unstable heartrate(s), the optimal phase of each cardiac cycle can be obtained.Moreover, in the process(es), requirement(s) for acquisition device(s)may be relatively low, and optimal phase(s) can be selected based ondata acquired under unsatisfactory scanning condition(s) and/oracquisition condition(s), thereby improving image quality, andcompensating (or reducing) the effects of poorly equipped equipment,poor scanning conditions, and the patient's motion.

It should be understood that although the various operations in theflowcharts of FIGS. 5-16 are displayed as indicated by the arrows, theseoperations are not necessarily performed in the order indicated by thearrows. Except as explicitly stated herein, there is no strict orderingof the execution of these operations, and these operations may beperformed in other orders. Moreover, at least a part of the operationsin FIGS. 5-16 may include a plurality of sub-operations or a pluralityof stages, and these sub-operations or stages are not necessarilyperformed at the same time, but may be performed at different times. Theoperations or stages are also not necessarily executed in sequence, butmay be performed alternately with other operations or at least a portionof sub-operations or stages of other operations.

FIG. 17 is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure.

In some embodiments, as shown in FIG. 17 , a block diagram illustratingan exemplary processing device for cardiac image reconstruction isprovided. The processing device 140 a may include a phase selectionmodule 17100, a first reconstruction module 17200, a cardiac motionparameter determination module 17300, a mean phase determination module17400, and a second reconstruction module 17500.

The phase selection module 17100 may be configured to select a pluralityof cardiac motion phases (e.g., at regular intervals).

The first reconstruction module 17200 may be configured to performreconstruction operation(s) according to scan data of the plurality ofcardiac motion phases, to obtain cardiac image(s) corresponding to theplurality of cardiac motion phases.

The cardiac motion parameter determination module 17300 may beconfigured to determine cardiac motion parameter(s) of the plurality ofcardiac motion phases according to the cardiac image(s) corresponding tothe plurality of cardiac motion phases.

A mean phase determination module 17400 may be configured to determine amean phase according to the cardiac motion parameters of the pluralityof cardiac motion phases.

The second reconstruction module 17500 may be configured to designatecardiac image(s) corresponding to the mean phase or the phase ofinterest as target cardiac image(s).

More descriptions of the processing device 140 a may be found in theabove descriptions of the cardiac image reconstruction process(es), andthe details are not repeated herein. Partial or all of the variousmodules in the processing device 140 a can be implemented as software,hardware, and combinations thereof. The modules may be embedded in orindependent of the processing device 140 in a computing device, or maybe stored as instructions in the memory of the computing device, so thatthe processing device 140 may call to perform corresponding operationsof the above modules.

In some embodiments, process(es) for determining a mean phase or a phaseof interest, and/or generating cardiac image(s) of interest are providedas illustrated above. The information regarding the mean phase or phaseof interest may be used to reconstruct target cardiac image(s).Process(es) for reconstructing cardiac image(s) may be described below.More descriptions of the phase selection module 17100 may be foundelsewhere in the present disclosure (e.g., FIG. 5 and descriptionsthereof). More descriptions of the first reconstruction module 17200 maybe found elsewhere in the present disclosure (e.g., FIG. 5 anddescriptions thereof). More descriptions of the cardiac motion parameterdetermination module 17300 may be found elsewhere in the presentdisclosure (e.g., FIGS. 5-11 and descriptions thereof). Moredescriptions of the mean phase determination module 17400 may be foundelsewhere in the present disclosure (e.g., FIGS. 5, 6, 12 anddescriptions thereof). More descriptions of the second reconstructionmodule 17500 may be found elsewhere in the present disclosure (e.g.,FIGS. 5, 13-16 and descriptions thereof).

It should be noted that the determination of the mean phase or the phaseof interest may be performed according to a machine learning algorithm(e.g., a deep learning algorithm). In some embodiments, the mean phaseor the phase of interest may be determined based on one or more deepneural networks. Merely by way of example, the mean phase or the phaseof interest may be determined based on a first deep nueral networkconfigured to extract one or more images of a region of interest (e.g.,vascular images of interest), and/or a second deep nueral networkconfigured to select the mean phase or the phase of interest. In someembodiments, the first deep nueral network may be trained usingprojection data (or raw data) and corresponding images of the region ofinterest. In some embodiments, images may be reconstructed based on theprojection data, and the region of interest (e.g., blood vessel(s)) maybe extracted from or labelled in the reconstructed image to obtain theimages of the region of interest. In some embodiments, projection dataassociated with the images of the region of interest may be generated byperforming a forward projection on the images of the region of interest.In some embodiments, in the training process of the first deep nueralnetwork, the raw data may be used as the input of the first deep nueralnetwork, and the projection data associated with the images of theregion of interest may be used as the output of the first deep nueralnetwork, and accordingly, the parameters (e.g., weight(s), bias(es),etc.) of hidden layer(s) in the first deep nueral network may beadjusted. In some embodiments, the second deep nueral network may betrained using the projection data associated with the images of theregion of interest corresponding to various cardiac motion phases. Insome embodiments, cardiac images of the various cardiac motion phasesmay be reconstructed based on the projection data associated with theimages of the region of interest corresponding to the various cardiacmotion phases. In some embodiments, the cardiac images may be lablled bya user (e.g., a doctor, an engineer, etc.). In some embodiments, thelabel(s) of cardiac images of the mean phase or the phase of interestmay be set as 1, while the label(s) of other cardiac images may be setas 0. In some embodiments, in the training process of the second deepnueral network, the projection data associated with the images of theregion of interest corresponding to various cardiac motion phases may beused as the input of the second deep nueral network, and the label(s) ofthe cardiac images of the various cardiac motion phases may be used asthe output of the second deep nueral network, and accordingly, theparameters (e.g., weight(s), bias(es), etc.) of hidden layer(s) in thesecond deep nueral network may be adjusted. In some embodiments, thefirst trained deep nueral network and the second trained deep nueralnetwork may be used to determine the mean phase or the phase of interestbased on the raw data.

FIG. 18 is a flowchart illustrating an exemplary process forreconstructing cardiac image(s) according to some embodiments of thepresent disclosure.

In an embodiment, as shown in FIG. 18 , an exemplary process for cardiacimage reconstruction is provided. The process 1800 may include thefollowing operations:

In 18102, a thoracic contour image may be obtained based on a maximumintensity projection of at least one preview image.

In some embodiments, the processing device 140 (e.g., the maximumintensity projection module 28100) may perform operation 18102. In someembodiments, a plurality of projection data in a plurality of cardiaccycles may be obtained. The plurality of projection data may begenerated by an imaging device (e.g., the scanner 110). The plurality ofprojection data may include a plurality of sub-sets of projection data.In some embodiments, a sub-set of projection data may correspond to acardiac motion phase of a cardiac cycle. In some embodiments, thepreview image(s) may be reconstructed in an initial field of view (FOV)based on at least a portion of the plurality of projection data. In someembodiments, the thoracic contour image may be obtained by performing amaximum intensity projection on the at least one preview image. In someembodiments, the preview image(s) may include image(s) in the transverseplane.

Specifically, in some embodiments, a preview image may be an originalimage generated by a computing device (e.g., the computing device 200)after a preliminary processing of the received data. In someembodiments, the preview image may be an original image reconstructed ina relatively large field of view (FOV) (or the initial FOV) (e.g., anFOV with a diameter of at least 500 mm). The original image may reflect(or illustrate) a structure of the entire thoracic cavity of an object(e.g., a patient). In the field of medical imaging technology, in orderto enhance (or improve) the imaging effect of a target site of apatient, a contrast agent may be usually injected or administered to thetarget site. The preview image(s) may be original image(s) of thethoracic cavity obtained based on image data collected after aninjection or administration of the contrast agent. In some embodiments,one or more preview images may be selected, and the preview image(s) maybe segmented respectively to obtain a first set of images reflectingposition(s) of thoracic bone(s) and location(s) of the contrast agent.In some embodiments, an opening operation may be performed on the firstset of images respectively to obtain a second set of images. The openingoperation may smooth contour(s) of the object or a portion thereof(e.g., the thoracic cavity), break narrow gap(s) (e.g., betweendifferent regions of the object), and/or eliminate fine protrusion(s)(e.g., fine protrusion region(s) generated after segmentation). In someembodiments, relatively thin edge(s) (e.g., thin edge region(s)generated after segmentation) may be removed by performing an openingoperation on the first set of images respectively, and the second set ofimages reflecting (or illustrating) the position(s) of the contrastagent may be obtained. A maximum intensity projection operation may beperformed on the first set of images and the second set of images,respectively, and initial thoracic contour image(s) may be obtained.

In some embodiments, a first initial thoracic contour image (e.g., amaximum intensity projection image of the first set of imagesillustrated in FIG. 19 ) may be obtained based on a maximum intensityprojection of the first set of images, and a second initial thoraciccontour image (e.g., a maximum intensity projection image of the secondset of images illustrated in FIG. 19 ) may be obtained based on amaximum intensity projection of the second set of images. In someembodiments, a thoracic contour image may be determined based on thefirst initial thoracic contour image and the second initial thoraciccontour image. For example, the thoracic contour image may be determinedbased on a difference between the first initial thoracic contour imageand the second initial thoracic contour image. In some embodiments, thethoracic contour image include information relating to at least aportion of the bones in the thorax. An exemplary thoracic contour imagemay be seen in FIG. 24 . In some embodiments, the opening operation mayinclude an erosion operation followed by a dilation operation. Moredescriptions of the determination of the thoracic contour image may befound elsewhere in the present disclosure (e.g., FIGS. 19 and 21 anddescriptions thereof).

In 18104, one or more positions of a thoracic contour boundary in thethoracic contour image may be determined based on the thoracic contourimage.

In some embodiments, the processing device 140 (e.g., the boundarydetermination module 28200) may perform operation 18104. A position ofthe thoracic contour boundary may refer to a position of an element oran element cluster in the thoracic contour image. In some embodiments,the processing device 140 may segment the thoracic contour image fordetermining the position(s) of the thoracic contour boundary.

Specifically, in some embodiments, the position(s) of the thoraciccontour boundary may include a first position of a leftmost boundary (ofthe thoracic contour boundary), a second position of a rightmostboundary (of the thoracic contour boundary), and/or a third position ofan uppermost boundary (of the thoracic contour boundary). In someembodiments, the first position of the leftmost boundary may be aphysical position of the leftmost boundary of the thoracic contourboundary; the second position of the rightmost boundary may be aphysical position of the rightmost boundary of the thoracic contourboundary; the third position of the uppermost boundary may be a physicalposition of the uppermost boundary of the thoracic contour boundary. Insome embodiments, the obtained thoracic contour image may be segmented,and a left thoracic contour image and a right thoracic contour image maybe obtained. Accordingly, the first position of the leftmost boundary,the second position of the rightmost boundary, and/or the third positionof the uppermost boundary may be determined based on the left thoraciccontour image, the right thoracic contour image, and the thoraciccontour image.

In some embodiments, the physical position may refer to a position in acoordinate system of the gantry. In some embodiments, a rotation centerof the gantry may be designated as the coordinate origin. In someembodiments, a position in a coordinate system of the image domain (alsoreferred to as image position) may be transformed to a physical positionin the coordinate system of the gantry (e.g., according to apredetermined transformation matrix or an equation (e.g., Equation (37)or (39))). More descriptions of the determination of the position(s) ofthe thoracic contour boundary may be found elsewhere in the presentdisclosure (e.g., FIGS. 20-21 and descriptions thereof).

In 18106, a reconstruction center may be determined based on the one ormore positions of the thoracic contour boundary.

In some embodiments, the processing device 140 (e.g., the centerreconstruction module 28300) may perform operation 18106. In someembodiments, the processing device 140 may determine the reconstructioncenter based on the one or more positions of the thoracic contourboundary. In some embodiments, the reconstruction center may be a centerof gravity, a geometrical center, or a center determined based on theposition(s) of the thoracic contour boundary according to a rule (seeEquations (56)-(57)).

Specifically, in some embodiments, a thoracic contour center may bedetermined based on the first position of the leftmost boundary, thesecond position of the rightmost boundary, and/or the third position ofthe uppermost boundary. In some embodiments, according to a position ofthe heart in the thoracic cavity, the reconstruction center may bedetermined as a position in an upper left region of the thoracic contourcenter.

In some embodiments, the upper left region of the thoracic contourcenter may refer to an upper left region in the image domain relative tothe thoracic contour center. More descriptions of the determination ofthe reconstruction center may be found elsewhere in the presentdisclosure (e.g., FIG. 21 and descriptions thereof).

In 18108, one or more target cardiac images may be reconstructed, in apreset FOV and at the reconstruction center.

In some embodiments, the processing device 140 (e.g., the imagereconstruction module 28400) may perform operation 18108. In someembodiments, the target cardiac image(s) may be reconstructed, in apreset FOV (also referred to as a preset reconstruction FOV) and at thereconstruction center, based on at least a portion of the plurality ofsub-sets of projection data. In some embodiments, the preset FOV may besmaller than the initial FOV. For example, the diameter of the initialFOV may be 200 mm, while the diameter of the preset FOV may be 150 mm.In some embodiments, the preset FOV may be set according to a defaultsetting of the imaging system 100 or preset by a user or operator viathe terminal(s) 130. In some embodiments, the reconstruction of thetarget cardiac image(s) may refer to a multi-phase reconstruction. Themulti-phase reconstruction may refer to the reconstruction of images ofcorresponding phases (e.g., the sampled cardiac motion phase illustratedin FIGS. 5, 6-7, and 11-12 ).

Specifically, in some embodiments, the multi-phase reconstruction may beperformed according to the determined reconstruction center and thepreset reconstruction FOV. In some embodiments, the coronary artery mayhave a curved shape in an axial direction of the thoracic cavity, andaccordingly, position(s) of the coronary artery in the axial directionof the thoracic cavity may be inconstant. Therefore, in someembodiments, the preset reconstruction FOV may not be set too small. Insome embodiments, an exemplary reconstruction FOV may have a diameter of80 mm. The reconstruction FOV may be an FOV for multi-phasereconstruction.

According to the process for reconstructing cardiac image(s), thethoracic contour image may be obtained based on preview image(s) and amaximum intensity projection of the preview image(s); first position(s)of a leftmost boundary, second position(s) of a rightmost boundary,and/or third position(s) of an uppermost boundary may be determinedaccording to the thoracic contour image; and a reconstruction center maybe determined based on the first position(s), second position(s), and/orthe third position(s). A multi-phase reconstruction may be performed onprojection data of an object according to the reconstruction center andthe preset reconstruction FOV to obtain cardiac image(s). Compared witha conventional reconstruction FOV with a diameter of 200 mm, cardiacimage(s) reconstructed based on the preset reconstruction FOVillustrated above may have a relatively high resolution (e.g., theresolution may be tripled). Besides, the determination process based onglobal element operation or local element operation may be optimizedusing the process above, and the operation efficiency may be improved.

FIG. 19 is a flowchart illustrating an exemplary process for determininga thoracic contour image according to some embodiments of the presentdisclosure. FIG. 22 is an exemplary maximum intensity projection imagerelating to bone(s) and a contrast agent according to some embodimentsof the present disclosure. FIG. 23 is an exemplary maximum intensityprojection image relating to a contrast agent according to someembodiments of the present disclosure. FIG. 24 is an exemplary thoraciccontour image according to some embodiments of the present disclosure.FIGS. 22-24 may be binary images. As shown in FIGS. 22-24 , element(s)that represent bone(s) and/or the contrast agent may have a first value,while element(s) that do not represent bone(s) and/or the contrast agentmay have a second value. The first value and the second value may bedifferent. For example, in FIGS. 22-24 , the first value may be 1, whilethe second value may be 0. As another example, the first value may be 0,while the second value may be 1. The values are merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure.

In some embodiments, as shown in FIGS. 19 and 22-24 , an exemplaryprocess for determining a thoracic contour image is provided. Theprocess 1900 may include the following operations:

In 19202, a first set of images may be obtained by segmenting thepreview image(s).

In some embodiments, the processing device 140 (e.g., the maximumintensity projection module 28100 (e.g., the first image segmenting unit29110)) may perform operation 19202. In some embodiments, a firstintermediate image may be obtained by segmenting a preview image, andaccordingly, a first set of images (including a plurality of firstintermediate images) may be obtained by segmenting a plurality ofpreview images.

Specifically, in some embodiments, the preview image(s) may be originalimage(s) obtained after preliminary processing of data received by acomputing device (e.g., the computing device 200). The preview image(s)may be original image(s) reconstructed based on a relatively large FOV(e.g., an FOV with a diameter larger than 500 mm). In some embodiments,the preview image(s) can show the structure of at least a portion of(e.g., the entire) thoracic cavity of the target object. In medicalimaging technology, in order to enhance the image effect of a targetsite of an object (e.g., a patient), a contrast agent may be injected oradministered to a target site (e.g., the coronary artery, theventricle). Data obtained after injection or administration of thecontrast agent may be preliminarily processed, and original image(s) ofthe thoracic cavity may be obtained. The original image(s) may beregarded as the preview image(s). In some embodiments, multiple previewimages may be selected, and the preview images may be segmented,respectively, to obtain a first set of images. The first set of imagesmay reflect the position(s) of bone(s) of the thoracic cavity and theposition(s) of the contrast agent.

In some embodiments, the first set of images may be obtained bysegmenting a plurality of preview images according to the followingequation:

$\begin{matrix}{{{IPB}1{z\left( {i,j} \right)}} = \left\{ {\begin{matrix}{1,\ {{{if}{\ }{{IPO}_{z}\left( {i,j} \right)}} > {{TB}1}}} \\{0,\ {{{if}\ {{IPO}_{z}\left( {i,j} \right)}} \leq {{TB}1}}}\end{matrix},} \right.} & {{Equation}(30)}\end{matrix}$

where IPO_(z)(i, j) denotes the element(s) of the zth preview image; TB1denotes a threshold relating to a high density structure of the targetobject; IPB1 denotes image(s) that are related to bone(s) and thecontrast agent and are obtained by segmenting preview image(s);IPB1_(z)(i, j) denotes the element(s) of the zth image of the imagesrelating to the bone(s) and the contrast agent.

In some embodiments, the first set of images may be binary images.

In 19204, a corresponding second set of images may be obtained byperforming an opening operation on the first set of images.

In some embodiments, the processing device 140 (e.g., the maximumintensity projection module 28100 (e.g., the opening operation unit29120)) may perform operation 19204. In some embodiments, acorresponding second intermediate image may be obtained by performing anopening operation on a first intermediate image, and accordingly, asecond set of images (including a plurality of second intermediateimages) may be obtained by performing an opening operation on the firstset of images respectively. In some embodiments, the second set ofimages may be binary images.

Specifically, in some embodiments, according to the obtained first setof images, an opening operation may be performed on the first set ofimages to obtain a second set of images. The opening operation may beperformed to smooth the contour(s) of the object or a portion thereof(e.g., a contour of an organ of the object), break narrow gap(s) (ordiscontinuities), and/or eliminate fine protrusion(s). Relatively thinedge(s) in the first set of images may be removed or reduced byperforming an opening operation on the first set of images separately,and a second set of images reflecting position(s) of the contrast agentmay be obtained.

In some embodiments, the second set of images may be obtained byperforming the opening operation on the first set of images according tothe following equation:

IPB2=(IPB1 ⊖se_b1)⊕se_b1,  Equation (31)

where IPB1 denotes image(s) that are related to bone(s) and the contrastagent (i.e., the first set of images) and are obtained by segmentingpreview image(s); IPB2 denotes image(s) relating to the contrast agent(i.e., the second set of images) that are obtained by performing anopening operation on the image(s) relating to bone(s) and the contrastagent; se_b1 denotes a structure element in morphology operation(s); edenotes a corrosion operation; ⊕ denotes an expansion operation.

In 19206, the thoracic contour image may be obtained by performing amaximum intensity projection on the first set of images and/or thecorresponding second set of images.

In some embodiments, the processing device 140 (e.g., the maximumintensity projection module 28100 (e.g., the maximum intensityprojection unit 29130)) may perform operation 19206.

Specifically, in some embodiments, the obtained first set of images andthe second set of images may be respectively subjected to the maximumintensity projection to obtain the thoracic contour image. The maximumintensity projection(s) may be generated based on element(s) having amaximum intensity (or density) along each projection ray directed to thepatient's target site. That is, if the projection ray passes throughoriginal image(s) of the patient's target site, the element(s) with thehighest intensity (or density) in the image(s) may be retained andprojected onto a two-dimensional plane, thereby forming a maximumintensity projection image of the patient's target site.

More specifically, in some embodiments, the first set of images may besubjected to the maximum intensity projection in the axial direction ofthe thoracic cavity to obtain a maximum intensity projection image (seeFIG. 22 ) of the first set of images. If the projection ray passesthrough the image(s) relating to the bone(s) and the contrast agent inthe axial direction of the thoracic cavity, the element(s) with thehighest intensity (or density) in the image(s) may be retained andprojected onto a two-dimensional plane, thereby forming a maximumintensity projection image of the first set of images.

In some embodiments, the first maximum intensity projection sub-unit30131 may perform a maximum intensity projection on the first set ofimages in the axial direction of the thoracic cavity to obtain themaximum intensity projection image of the first set of images. In someembodiments, the maximum intensity projection image of the first set ofimages may be a binary image.

In some embodiments, the maximum intensity projection image of the firstset of images may be obtained by projection in the axial direction ofthe thoracic cavity, which can be represented as the following equation:

IM_(B1)(i,j)=max(IPB1_(z)(i,j)),z=1,2, . . . N,  Equation (32)

where IPB1_(z)(i, j) denotes the element(s) of the zth image of theimages relating to the bone(s) and the contrast agent (i.e., the firstset of images); N denotes the number of images relating to the bone(s)and the contrast agent; IM_(B1) denotes the maximum intensity projectionimage of the image(s) relating to the bone(s) and the contrast agent(see FIG. 22 ); IM_(B1)(i, j) denotes the gray levels of the element(s)of the maximum intensity projection image of the image(s) relating tothe bone(s) and the contrast agent.

A maximum intensity projection may be performed on the second set ofimage(s) in the axial direction of the thoracic cavity to obtain amaximum intensity projection image (see FIG. 23 ) of the second set ofimages. If the projection ray passes through the image(s) relating tothe contrast agent in the axial direction of the thoracic cavity, theelement(s) with the highest intensity (or density) in the image(s) maybe retained and projected onto a two-dimensional plane, thereby forminga maximum intensity projection image of the second set of images.

In some embodiments, the second maximum intensity projection sub-unit30132 may perform a maximum intensity projection on the second set ofimages in the axial direction of the thoracic cavity to obtain themaximum intensity projection image of the second set of images. In someembodiments, the maximum intensity projection image of the second set ofimages may be a binary image.

In some embodiments, the maximum intensity projection image of thesecond set of images may be obtained by projection in the axialdirection of the thoracic cavity, which can be represented as thefollowing equation:

IM_(B2)(i,j)=max(IPB2_(z)(i,j)),z=1,2, . . . N,  Equation (33)

where IPB2_(z)(i, j) denotes the element(s) of the zth image of theimages relating to the contrast agent (i.e., the second set of images);N denotes the number of the images relating to the contrast agent;IM_(B2) denotes the maximum intensity projection image of the imagesrelating to the contrast agent; IM_(B2)(i, j) denotes the element(s) ofthe maximum intensity projection image of the images relating to thecontrast agent.

In some embodiments, a difference between the maximum intensityprojection image of the first set of images and the maximum intensityprojection image of the second set of images may be determined as athoracic contour image (see FIG. 24 ).

In some embodiments, the difference may be a subtraction between themaximum intensity projection image of the first set of images and themaximum intensity projection image of the second set of images.Specifically, the difference may be a subtraction between values of theelements of the maximum intensity projection image of the first set ofimages and values of corresponding elements of the maximum intensityprojection image of the second set of images. In some embodiments, thedifference determination sub-unit 30133 may determine the differencebetween the maximum intensity projection image of the first set ofimages and the maximum intensity projection image of the second set ofimages to obtain a thoracic contour image. In some embodiments, thethoracic contour image may be a binary image.

In some embodiments, the thoracic contour image may be determined basedon the difference between the maximum intensity projection image of thefirst set of images and the maximum intensity projection image of thesecond set of images, which may be represented as the followingequation:

$\begin{matrix}{{{IM}_{st}\left( {i,j} \right)} = \left\{ {\begin{matrix}{{{IM}_{B1}\left( {i,j} \right)}\ ,\ {{{if}\ {{IM}_{B2}\left( {i,j} \right)}} \leq 0}} \\{0,\ {{{if}{{IM}_{B2}\left( {i,j} \right)}} > 0}}\end{matrix}.} \right.} & {{Equation}(34)}\end{matrix}$

where IM_(B1) denotes the maximum intensity projection image of theimages relating to the bone(s) and the contrast agent; M_(B1)(i, j)denotes the element(s) of the maximum intensity projection image of theimages relating to the bone(s) and the contrast agent; IM_(B2) denotesthe maximum intensity projection image of the images relating to thecontrast agent (see FIG. 23 ); IM_(B2)(i, j) denotes the element(s) ofthe maximum intensity projection image of the images relating to thecontrast agent; IM_(st) denotes a thoracic contour image; IM_(st)(i, j)denotes the element(s) of the thoracic contour image.

According to the process for determining a thoracic contour image, aplurality of preview images may be segmented to obtain a first set ofimages; an opening operation may be performed on the first set of imagesto obtain a second set of images; a maximum intensity projection may beperformed respectively on the first set of images and the second set ofimages; and/or a subtraction between the maximum intensity projectionimage of the first set of images and the maximum intensity projectionimage of the second set of images may be determined to generate thethoracic contour image. The thoracic contour image can be obtained usingthe maximum intensity projection more accurately, which can remove theinterferences of relatively thin edge(s) more effectively.

FIG. 20 is a flowchart illustrating an exemplary process for determiningone or more positions of a thoracic contour boundary according to someembodiments of the present disclosure. FIG. 25 is an exemplary leftthoracic contour image according to some embodiments of the presentdisclosure. FIG. 26 is an exemplary right thoracic contour imageaccording to some embodiments of the present disclosure. FIG. 27 is animage illustrating a region to be analyzed according to some embodimentsof the present disclosure.

In some embodiments, as shown in FIGS. 20 and 25-27 , an exemplaryprocess for determining one or more positions of a thoracic contourboundary in a thoracic contour image is provided. The process mayinclude the following operations:

In 20302, a left thoracic contour image and/or a right thoracic contourimage may be obtained by segmenting the at least one thoracic contourimage.

In some embodiments, the processing device 140 (e.g., the boundarydetermination module 28200 (e.g., the second image segmentation unit31210)) may perform operation 20302. In some embodiments, the positionsof the thoracic contour boundary may refer to the boundary positions ofthe thoracic contour image. The position(s) (or physical position(s)) ofthoracic contour boundary illustrated below may refer to relativeposition(s) in the thoracic contour image.

Specifically, in some embodiments, the boundary positions of thethoracic contour image may include a leftmost boundary position, arightmost boundary position, and/or an uppermost boundary position. Insome embodiments, the leftmost boundary position may be a physicalposition of the leftmost boundary of the thoracic contour image. Therightmost boundary position may be a physical position of the rightmostboundary of the thoracic contour image. The uppermost boundary positionmay be a physical position of the uppermost boundary of the thoraciccontour image. In some embodiments, according to the obtained thoraciccontour image, segmentation may be performed on the thoracic contourimage, and a left thoracic contour image (see FIG. 25 ) and/or a rightthoracic contour image (see FIG. 26 ) may be obtained.

In some embodiments, the segmentation of the thoracic contour image maybe performed based on the positions of the elements of the thoraciccontour image. An exemplary segmentation of the thoracic contour imagemay be performed according to Equations (35)-(36).

In some embodiments, the segmentation of the thoracic contour image toobtain the left thoracic contour image and the right thoracic contourimage may be represented as the following equation:

$\begin{matrix}{{{IM}_{1}\left( {i,j} \right)} = \left\{ {\begin{matrix}{{{IM}_{st}\left( {i,j} \right)},} & {{{if}j} \leq {M/2}} \\{0,} & {else}\end{matrix},} \right.} & {{Equation}(35)}\end{matrix}$ $\begin{matrix}{{{IM}_{r}\left( {i,j} \right)} = \left\{ {\begin{matrix}{{{IM}_{st}\left( {i,j} \right)},} & {{{if}j} \geq {M/2}} \\{0,} & {else}\end{matrix},} \right.} & {{Equation}(36)}\end{matrix}$

where IM_(st) denotes a thoracic contour image; IM_(st)(i, j) denotesthe element(s) of the thoracic contour image; IM_(l) denotes the leftthoracic contour image; IM_(l)(i, j) denotes the element(s) of the leftthoracic contour image; IM_(r) denotes the right thoracic contour image;IM_(r)(i, j) denotes the element(s) of the right thoracic contour image;M denotes the matrix size of the thoracic contour image.

In 20304, a first position of the leftmost boundary of the thoraciccontour boundary may be determined based on the left thoracic contourimage.

In some embodiments, the processing device 140 (e.g., the boundarydetermination module 28200 (e.g., the left boundary determination unit31220)) may perform operation 20304.

Specifically, in some embodiments, a largest connected domain (alsoreferred as a first maximum connected domain) may be determinedaccording to the left thoracic contour image, and elements in thelargest connected domain of the left thoracic contour image may beselected. The connected domain may correspond to a region in a complexplane. If a simple closed curve is used in the complex plane, and theinternal of the closed curve always belongs to the region, then theregion is a connected domain. The upper part of the right coronary inthe largest connected domain of the left thoracic contour image may beselected, and the leftmost boundary position may be determined.

In some embodiments, the processing device 140 may determine a firstmaximum connected domain in the left thoracic contour image, and selecta first target region including at least a portion of a left thoraciccontour in the first maximum connected domain. In some embodiments, thefirst target region may include elements in the first maximum connecteddomain of the left thoracic contour image. In some embodiments, the atleast a portion of the left thoracic contour may include a rightcoronary. In some embodiments, the processing device 140 may select afirst region of interest including the right coronary in the firsttarget region, and determine the first position of the leftmost boundaryof the thoracic contour boundary based on the first region of interest.In some embodiments, the first region of interest may include the upperpart of the right coronary in the first maximum connected domain of theleft thoracic contour image.

In some embodiments, the first connected domain determination sub-unit32221 may determine the maximum connected domain according to the leftthoracic contour image, and select elements in the first maximumconnected domain of the left thoracic contour image. In someembodiments, the left boundary determination sub-unit 32222 may selectelements in an upper part of the right coronary in the first maximumconnected domain of the left thoracic contour image, and determine (thefirst position of) the leftmost boundary.

In some embodiments, the leftmost boundary position may be determinedaccording to the following equations:

$\begin{matrix}{{{{Po}s_{l}} = {- {\left( {{Pix_{l}} - \left( \frac{M - 1}{2} \right)} \right) \times {Spacing}_{pre}}}},} & {{Equation}(37)}\end{matrix}$ $\begin{matrix}{{{Pix}_{l} = {\min\left( {\left. j \middle| \left( {i,j} \right) \right. \in {{IM}_{1}\left( {i,j} \right)}} \right)}},} & {{Equation}(38)}\end{matrix}$

where Pix_(l) denotes the leftmost element position of the left thoraciccontour image (i.e., the image position of the leftmost element);Spacing_(pre) denotes the resolution of the elements (of the leftthoracic contour image); Pos_(l) denotes the leftmost boundary positionof the thoracic contour image (i.e., the physical position of theleftmost boundary); M denotes the matrix size of the thoracic contourimage; IM_(l) denotes the left thoracic contour image.

In some embodiments, the resolution of the left thoracic contour imagemay be the same as the resolution of the right thoracic contour image,the resolution of the thoracic contour image, and/or the resolution ofthe preview image(s). In some embodiments, matrix size of the leftthoracic contour image may be the same as the matrix size of the rightthoracic contour image, the matrix size of the thoracic contour image,and/or the matrix size of the preview image(s).

In 20306, a second position of the rightmost boundary of the thoraciccontour boundary may be determined based on the right thoracic contourimage.

In some embodiments, the processing device 140 (e.g., the boundarydetermination module 28200 (e.g., the right boundary determination unit31230)) may perform operation 20306.

Specifically, in some embodiments, a largest connected domain (alsoreferred as a second maximum connected domain) may be determinedaccording to the right thoracic contour image, and elements in thelargest connected domain of the right thoracic contour image may beselected. The connected domain may correspond to a region in a complexplane. If a simple closed curve, which may form a region surrounded bythe curve, is used in the complex plane, and the internal of the closedcurve always belongs to the region, then the region is a connecteddomain. The upper part of the right coronary in the largest connecteddomain of the right thoracic contour image may be selected, and therightmost boundary position may be determined.

In some embodiments, the processing device 140 may determine a secondmaximum connected domain in the right thoracic contour image, and selecta second target region including at least a portion of a right thoraciccontour in the second maximum connected domain. In some embodiments, thesecond target region may include elements in the second maximumconnected domain of the right thoracic contour image. In someembodiments, the at least a portion of the right thoracic contour mayinclude a right coronary. In some embodiments, the processing device 140may select a second region of interest including the right coronary inthe second target region, and determine the second position of therightmost boundary of the thoracic contour boundary based on the secondregion of interest. In some embodiments, the second region of interestmay include the upper part of the right coronary in the second maximumconnected domain of the right thoracic contour image.

In some embodiments, the second connected domain determination sub-unit33231 may determine the maximum connected domain according to the rightthoracic contour image, and select elements within the second maximumconnected domain of the right thoracic contour image. In someembodiments, the right boundary determination sub-unit 33232 may selectelements in an upper part of the right coronary in the second maximumconnected domain of the right thoracic contour image, and determine therightmost boundary position.

In some embodiments, the rightmost boundary position may be determinedaccording to the following equations:

$\begin{matrix}{{{{Po}s_{r}} = {- {\left( {{Pix_{r}} - \left( \frac{M - 1}{2} \right)} \right) \times {Spacing}_{pre}}}},} & {{Equation}(39)}\end{matrix}$ $\begin{matrix}{{{Pix}_{r} = {\max\left( {\left. j \middle| \left( {i,j} \right) \right. \in {{IM}_{r}\left( {i,j} \right)}} \right)}},} & {{Equation}(40)}\end{matrix}$

where Pix_(r) denotes the rightmost element position of the rightthoracic contour image (i.e., the image position of the rightmostelement); Spacing_(pre) denotes the resolution of the elements (of theright thoracic contour image); Pos_(r) denotes the rightmost boundaryposition of the thoracic contour image (i.e., the physical position ofthe rightmost boundary); M denotes the matrix size of the thoraciccontour image; IM_(r) denotes the right thoracic contour image.

In 20308, a third position of the uppermost boundary of the thoraciccontour boundary may be determined based on the at least one thoraciccontour image.

In some embodiments, the processing device 140 (e.g., the boundarydetermination module 28200 (e.g., the upper boundary determination unit31240)) may perform operation 20308.

Specifically, in some embodiments, a region to be analyzed (e.g., theregion to be analyzed in FIG. 27 ) may be determined in the thoraciccontour image according to the rightmost position of the left thoraciccontour image, the leftmost position of the right thoracic contourimage, and the lowest position of the second set of images. The secondset of images may include image(s) reflecting the location(s) of theelements representing the contrast agent. The largest connected domainmay be determined according to the region to be analyzed, and elementsin the largest connected domain may be selected. According to theelements in the largest connected domain of the region to be analyzed,the uppermost boundary position may be determined.

In some embodiments, the regions to be analyzed may also be referred toas a candidate region of interest. In some embodiments, the candidateregion of interest determination sub-unit 34241 may determine the regionto be analyzed in the thoracic contour image according to the rightmostposition of the left thoracic contour image, the leftmost position ofthe right thoracic contour image, and the lowest position of the secondset of images. In some embodiments, the third connected domaindetermination sub-unit 34242 may determine a third maximum connecteddomain according to the region to be analyzed, and select elementswithin the third maximum connected domain. In some embodiments, theupper boundary determination sub-unit 34243 may determine the uppermostboundary position according to the elements of the thoracic contourimage in the third maximum connected domain of the candidate region ofinterest. In some embodiments, the processing device 140 may determinethe third maximum connected domain in the candidate region of interest,and select a third target region of the thoracic contour image in thethird maximum connected domain. The third target region may include atleast a portion of a thoracic contour. In some embodiments, the thirdtarget region may include elements of the thoracic contour image in thethird maximum connected domain of the candidate region of interest. Insome embodiments, the region to be analyzed (also referred to as thecandidate region of interest) may include one or more sternums. In someembodiments, the determination of the uppermost boundary position basedon the region to be analyzed may eliminate or reduce the effect of oneor more regions above the sternum(s) on the further segmentation (orextraction) of the right coronary.

In some embodiments, the region to be analyzed may be determined in thethoracic contour image based on the rightmost position of the leftthoracic contour image, the leftmost position of the right thoraciccontour image, and the lowest position of the second set of imagesaccording to the following equation:

Irib=IM_(st)(1:id,id1:id2),  Equation (41)

where id denotes the lowest y position of the set of images relating tothe contrast agent; id1 denotes the rightmost x position of the leftthoracic contour image; id2 denotes the leftmost x position of the rightthoracic contour image; Irib denotes the region to be analyzed; IM_(st)denotes the thoracic contour image.

In some embodiments, the uppermost boundary position may be determinedbased on the thoracic contour image within the largest connected domainaccording to the following equation:

$\begin{matrix}{{{Pos_{up}} = {\left( {{Pix_{up}} - \left( \frac{M - 1}{2} \right)} \right) \times {Spacing}_{pre}}},} & {{Equation}(42)}\end{matrix}$

where Pix_(up) denotes the uppermost element position of the thoraciccontour image (i.e., the image position of the uppermost element);Spacing_(pre) denotes the resolution of the elements (in the thoraciccontour image); Pos_(up) denotes the uppermost boundary position of thethoracic contour image (i.e., the physical position of the uppermostboundary); M denotes the matrix size of the thoracic contour image.

According to the process for determining the boundary position(s) of thethoracic contour image, the thoracic contour image may be segmented, anda left thoracic contour image and a right thoracic contour image may beobtained; the leftmost boundary position, the rightmost boundaryposition, and the uppermost boundary position may be determinedaccording to the left thoracic contour image, the right thoracic contourimage, and the thoracic contour image. Through the determination of theboundary position(s) of the thoracic cavity boundary, the centerposition of the thoracic contour can be determined more accurately, andaccordingly, the position of the heart in the thoracic cavity can bedetermined more accurately, so that the determination of the heartposition may be more accurate.

FIG. 21 is a flowchart illustrating an exemplary process forreconstructing cardiac image(s) according to some embodiments of thepresent disclosure.

In some embodiments, as shown in FIG. 21 , an exemplary process forcardiac image reconstruction is provided. The process 2100 may includethe following operations:

In 21402, a first set of images may be obtained by segmenting thepreview image(s).

In some embodiments, the processing device 140 (e.g., the maximumintensity projection module 28100 (e.g., the first image segmenting unit29110)) may perform operation 21402. In some embodiments, a firstintermediate image may be obtained by segmenting a preview image, andaccordingly, a first set of images (including a plurality of firstintermediate images) may be obtained by segmenting a plurality ofpreview images.

Specifically, in some embodiments, the preview image(s) may be originalimage(s) obtained after preliminary processing of data received by acomputing device (e.g., the computing device 200). The preview image(s)may be original image(s) reconstructed based on a relatively large FOV(e.g., an FOV with a diameter larger than 500 mm). In some embodiments,the preview image(s) can show the structure of at least a portion of(e.g., the entire) thoracic cavity of the target object. In medicalimaging technology, in order to enhance the imaging effect of a targetsite of an object (e.g., a patient), a contrast agent may be injected oradministered to a target site (e.g., the coronary artery, theventricle). Data obtained after injection or administration of thecontrast agent may be preliminarily processed, and original image(s) ofthe thoracic cavity may be obtained. The original image(s) may beregarded as the preview image(s). In some embodiments, multiple previewimages may be selected, and the preview images may be segmented,respectively, to obtain a first set of images. The first set of imagesmay reflect the position(s) of bone(s) of the thoracic cavity and theposition(s) of the contrast agent.

In some embodiments, the first set of images may be obtained bysegmenting a plurality of preview images according to the followingequation:

$\begin{matrix}{{{IPB}1{z\left( {i,j} \right)}} = \left\{ {\begin{matrix}{1,\ {{{if}\ {{IPO}_{z}\left( {i,j} \right)}} > {{TB}1}}} \\{0,\ {{{if}\ {{IPO}_{z}\left( {i,j} \right)}} \leq {{TB}1}}}\end{matrix},} \right.} & {{Equation}(43)}\end{matrix}$

where IPO_(z)(i, j) denotes the element(s) of the zth preview image; TB1denotes a threshold relating to a high density structure of the targetobject; IPB1 denotes image(s) that are related to bone(s) and thecontrast agent and are obtained by segmenting preview image(s);IPB1_(z)(i, j) denotes the element(s) of the zth image of the imagesrelating to the bone(s) and the contrast agent.

In some embodiments, the first set of images may be binary images.

In 21404, a corresponding second set of images may be obtained byperforming an opening operation on the first set of images.

In some embodiments, the processing device 140 (e.g., the maximumintensity projection module 28100 (e.g., the opening operation unit29120)) may perform operation 21404. In some embodiments, acorresponding second intermediate image may be obtained by performing anopening operation on a first intermediate image, and accordingly, asecond set of images (including a plurality of second intermediateimages) may be obtained by performing an opening operation on the firstset of images respectively. In some embodiments, the second set ofimages may be binary images.

Specifically, in some embodiments, according to the obtained first setof images, an opening operation may be performed on the first set ofimages to obtain a second set of images. The opening operation may beperformed to smooth the contour(s) of the object or a portion thereof(e.g., a contour of an organ of the object), break narrow gap(s) (ordiscontinuities), and/or eliminate fine protrusion(s). Relatively thinedge(s) in the first set of images may be removed or reduced byperforming an opening operation on the first set of images separately,and a second set of images reflecting position(s) of the contrast agentmay be obtained.

In some embodiments, the second set of images may be obtained byperforming the opening operation on the first set of images according tothe following equation:

IPB2=(IPB1⊖se_b1)⊕se_b1,  Equation (44)

where IPB1 denotes image(s) that are related to bone(s) and the contrastagent (i.e., the first set of images) and are obtained by segmentingpreview image(s); IPB2 denotes image(s) relating to the contrast agent(i.e., the second set of images) that are obtained by performing anopening operation on the image(s) relating to bone(s) and the contrastagent; se_b1 denotes a structure element in morphology operation(s); ⊖denotes a corrosion operation; ⊕ denotes an expansion operation.

In 21406, the thoracic contour image may be obtained by performing amaximum intensity projection on the first set of images and/or thecorresponding second set of images.

In some embodiments, the processing device 140 (e.g., the maximumintensity projection module 28100 (e.g., the maximum intensityprojection unit 29130)) may perform operation 21406.

Specifically, in some embodiments, the obtained first set of images andthe second set of images may be respectively subjected to the maximumintensity projection to obtain the thoracic contour image. The maximumintensity projection(s) may be generated based on element(s) having amaximum intensity (or density) along each projection ray directed to thepatient's target site. That is, if the projection ray passes throughoriginal image(s) of the patient's target site, the element(s) with thehighest intensity (or density) in the image(s) may be retained andprojected onto a two-dimensional plane, thereby forming a maximumintensity projection image of the patient's target site.

More specifically, in some embodiments, the first set of images may besubjected to the maximum intensity projection in the axial direction ofthe thoracic cavity to obtain a maximum intensity projection image (seeFIG. 22 ) of the first set of images. If the projection ray passesthrough the image(s) relating to the bone(s) and the contrast agent inthe axial direction of the thoracic cavity, the element(s) with thehighest intensity (or density) in the image(s) may be retained andprojected onto a two-dimensional plane, thereby forming a maximumintensity projection image of the first set of images.

In some embodiments, the maximum intensity projection image of the firstset of images may be obtained by projection in the axial direction ofthe thoracic cavity, which can be represented as the following equation:

IM_(B1)(i,j)=max(IPB1_(z)(i,j)),z=1,2, . . . N,  Equation (45)

where IPB1_(z)(i, j) denotes the element(s) of the zth image of theimages relating to the bone(s) and the contrast agent (i.e., the firstset of images); N denotes the number of images relating to the bone(s)and the contrast agent; IM_(B1) denotes the maximum intensity projectionimage of the image(s) relating to the bone(s) and the contrast agent(see FIG. 22 ); IM_(B1)(i, j) denotes the gray levels of the element(s)of the maximum intensity projection image of the image(s) relating tothe bone(s) and the contrast agent.

A maximum intensity projection may be performed on the second set ofimage(s) in the axial direction of the thoracic cavity to obtain amaximum intensity projection image (see FIG. 23 ) of the second set ofimages. If the projection ray passes through the image(s) relating tothe contrast agent in the axial direction of the thoracic cavity, theelement(s) with the highest intensity (or density) in the image(s) maybe retained and projected onto a two-dimensional plane, thereby forminga maximum intensity projection image of the second set of images.

In some embodiments, the maximum intensity projection image of thesecond set of images may be obtained by projection in the axialdirection of the thoracic cavity, which can be represented as thefollowing equation:

IM_(B2)(i,j)=max(IPB2_(z)(i,j)),z=1,2, . . . N,  Equation (46)

where IPB2_(z)(i, j) denotes the element(s) of the zth image of theimages relating to the contrast agent (i.e., the second set of images);N denotes the number of the images relating to the contrast agent;IM_(B2) denotes the maximum intensity projection image of the imagesrelating to the contrast agent; IM_(B2)(i, j) denotes the element(s) ofthe maximum intensity projection image of the images relating to thecontrast agent.

In some embodiments, a difference between the maximum intensityprojection image of the first set of images and the maximum intensityprojection image of the second set of images may be determined as athoracic contour image (see FIG. 24 ).

In some embodiments, the difference may be a subtraction between themaximum intensity projection image of the first set of images and themaximum intensity projection image of the second set of images.

In some embodiments, the thoracic contour image may be determined basedon the difference between the maximum intensity projection image of thefirst set of images and the maximum intensity projection image of thesecond set of images, which may be represented as the followingequation:

$\begin{matrix}{{{IM}_{st}\left( {i,j} \right)} = \left\{ {\begin{matrix}{{{IM}_{B1}\left( {i,j} \right)}\ ,{{{if}\ {{IM}_{B2}\left( {i,j} \right)}} \leq 0}} \\{0,\ {{{if}\ {{IM}_{B2}\left( {i,j} \right)}} > 0}}\end{matrix},} \right.} & {{Equation}(47)}\end{matrix}$

where IM_(B1) denotes the maximum intensity projection image of theimages relating to the bone(s) and the contrast agent; IM_(B1)(i, j)denotes the element(s) of the maximum intensity projection image of theimages relating to the bone(s) and the contrast agent; IM_(B2) denotesthe maximum intensity projection image of the images relating to thecontrast agent (see FIG. 23 ); IM₂(i, j) denotes the element(s) of themaximum intensity projection image of the images relating to thecontrast agent; IM_(st) denotes a thoracic contour image; IM_(st)(i, j)denotes the element(s) of the thoracic contour image.

In 21408, a left thoracic contour image and/or a right thoracic contourimage may be obtained by segmenting the at least one thoracic contourimage.

In some embodiments, the processing device 140 (e.g., the boundarydetermination module 28200 (e.g., the second image segmentation unit31210)) may perform operation 21408. In some embodiments, the positionsof the thoracic contour boundary may refer to the boundary positions ofthe thoracic contour image. The position(s) (or physical position(s)) ofthoracic contour boundary illustrated below may refer to relativeposition(s) in the thoracic contour image.

Specifically, in some embodiments, the boundary positions of thethoracic contour image may include a leftmost boundary position, arightmost boundary position, and/or an uppermost boundary position. Insome embodiments, the leftmost boundary position may be a physicalposition of the leftmost boundary of the thoracic contour image. Therightmost boundary position may be a physical position of the rightmostboundary of the thoracic contour image. The uppermost boundary positionmay be a physical position of the uppermost boundary of the thoraciccontour image. In some embodiments, according to the obtained thoraciccontour image, segmentation may be performed on the thoracic contourimage, and a left thoracic contour image and/or a right thoracic contourimage may be obtained.

In some embodiments, the segmentation of the thoracic contour image toobtain the left thoracic contour image and the right thoracic contourimage may be represented as the following equation:

$\begin{matrix}{{{IM}_{1}\left( {i,j} \right)} = \left\{ {\begin{matrix}{{{IM}_{st}\left( {i,j} \right)},} & {{{if}j} \leq {M/2}} \\{0,} & {else}\end{matrix},} \right.} & {{Equation}(48)}\end{matrix}$ $\begin{matrix}{{{IM}_{r}\left( {i,j} \right)} = \left\{ {\begin{matrix}{{{IM}_{st}\left( {i,j} \right)},} & {{{if}j} \geq {M/2}} \\{0,} & {else}\end{matrix},} \right.} & {{Equation}(49)}\end{matrix}$

where IM_(st) denotes a thoracic contour image; IM_(st)(i, j) denotesthe element(s) of the thoracic contour image; IM_(l) denotes the leftthoracic contour image; IM_(l)(i, j) denotes the element(s) of the leftthoracic contour image; IM_(r) denotes the right thoracic contour image;IM_(r)(i, j) denotes the element(s) of the right thoracic contour image;M denotes the matrix size of the thoracic contour image.

In 21410, a first position of the leftmost boundary of the thoraciccontour boundary may be determined based on the left thoracic contourimage.

In some embodiments, the processing device 140 (e.g., the boundarydetermination module 28200 (e.g., the left boundary determination unit31220)) may perform operation 21410.

Specifically, in some embodiments, a largest connected domain may bedetermined according to the left thoracic contour image, and elements inthe largest connected domain of the left thoracic contour image may beselected. The connected domain may correspond to a region in a complexplane. If a simple closed curve is used in the complex plane, and theinternal of the closed curve always belongs to the region, then theregion is a connected domain. The upper part of the right coronary inthe largest connected domain of the left thoracic contour image may beselected, and the leftmost boundary position may be determined.

In some embodiments, the leftmost boundary position may be determinedaccording to the following equations:

$\begin{matrix}{{{{Po}s_{l}} = {- {\left( {{Pix_{l}} - \left( \frac{M - 1}{2} \right)} \right) \times {Spacing}_{pre}}}},} & {{Equation}(50)}\end{matrix}$ $\begin{matrix}{{{Pix}_{l} = {\min\left( {\left. j \middle| \left( {i,j} \right) \right. \in {{IM}_{1}\left( {i,j} \right)}} \right)}},} & {{Equation}(51)}\end{matrix}$

where Pix_(l) denotes the leftmost element position of the left thoraciccontour image (i.e., the image position of the leftmost element);Spacing_(pre) denotes the resolution of the elements (of the leftthoracic contour image); Pos_(l) denotes the leftmost boundary positionof the thoracic contour image (i.e., the physical position of theleftmost boundary); M denotes the matrix size of the thoracic contourimage; IM_(l) denotes the left thoracic contour image.

In 21412, a second position of the rightmost boundary of the thoraciccontour boundary may be determined based on the right thoracic contourimage.

In some embodiments, the processing device 140 (e.g., the centerreconstruction module 28300 (e.g., the right boundary determination unit31230)) may perform operation 21412.

Specifically, in some embodiments, a largest connected domain may bedetermined according to the left thoracic contour image, and elements inthe largest connected domain of the right thoracic contour image may beselected. The connected domain may correspond to a region in a complexplane. If a simple closed curve is used in the complex plane, and theinternal of the closed curve always belongs to the region, then theregion is a connected domain. The upper part of the right coronary inthe largest connected domain of the right thoracic contour image may beselected, and the rightmost boundary position may be determined.

In some embodiments, the rightmost boundary position may be determinedaccording to the following equations:

$\begin{matrix}{{{{Po}s_{r}} = {- {\left( {{Pix_{r}} - \left( \frac{M - 1}{2} \right)} \right) \times {Spacing}_{pre}}}},} & {{Equation}(52)}\end{matrix}$ $\begin{matrix}{{{Pix}_{r} = {\max\left( {\left. j \middle| \left( {i,j} \right) \right. \in {{IM}_{r}\left( {i,j} \right)}} \right)}},} & {{Equation}(53)}\end{matrix}$

where Pix_(r) denotes the rightmost element position of the rightthoracic contour image (i.e., the image position of the rightmostelement); Spacing_(pre) denotes the resolution of the elements (of theright thoracic contour image); Pos_(r) denotes the rightmost boundaryposition of the thoracic contour image (i.e., the physical position ofthe rightmost boundary); M denotes the matrix size of the thoraciccontour image; IM_(r) denotes the right thoracic contour image.

In 21414, a third position of the uppermost boundary of the thoraciccontour boundary may be determined based on the at least one thoraciccontour image.

In some embodiments, the processing device 140 (e.g., the centerreconstruction module 28300 (e.g., the upper boundary determination unit31240)) may perform operation 21414.

Specifically, in some embodiments, a region to be analyzed may bedetermined in the thoracic contour image according to the rightmostposition of the left thoracic contour image, the leftmost position ofthe right thoracic contour image, and the lowest position of the secondset of images. The second set of images may include image(s) reflectingthe location(s) of the elements representing the contrast agent. Thelargest connected domain may be determined according to the region to beanalyzed, and elements in the largest connected domain of the thoraciccontour image may be selected. According to the elements in the largestconnected domain of the thoracic contour image, the uppermost boundaryposition may be determined.

In some embodiments, the region to be analyzed may be determined in thethoracic contour image based on the rightmost position of the leftthoracic contour image, the leftmost position of the right thoraciccontour image, and the lowest position of the second set of imagesaccording to the following equation:

Irib=IM_(st)(1:id,id1:id2),  Equation (54)

where id denotes the lowest y position of the set of images relating tothe contrast agent; id1 denotes the rightmost x position of the leftthoracic contour image; id2 denotes the leftmost x position of the rightthoracic contour image; Irib denotes the region to be analyzed; IM_(st)denotes the thoracic contour image.

In some embodiments, the uppermost boundary position may be determinedbased on the thoracic contour image within the largest connected domainaccording to the following equation:

$\begin{matrix}{{{{Po}s_{up}} = {\left( {{Pix_{up}} - \left( \frac{M - 1}{2} \right)} \right) \times {Spacing}_{pre}}},} & {{Equation}(55)}\end{matrix}$

where Pix_(up) denotes the uppermost element position of the thoraciccontour image (i.e., the image position of the uppermost element);Spacing_(pre) denotes the resolution of the elements (in the thoraciccontour image); Pos_(up) denotes the uppermost boundary position of thethoracic contour image (i.e., the physical position of the uppermostboundary); M denotes the matrix size of the thoracic contour image.

In 21416, a reconstruction center may be determined based on the one ormore positions of the thoracic contour boundary.

In some embodiments, the processing device 140 (e.g., the centerreconstruction module 28300) may perform operation 21416. In someembodiments, the processing device 140 may determine the reconstructioncenter based on the one or more positions of the thoracic contourboundary. In some embodiments, the reconstruction center may be a centerof gravity, a geometrical center, or a center determined based on theposition(s) of the thoracic contour boundary according to a rule (seeEquations (56)-(57)).

Specifically, in some embodiments, a thoracic contour center may bedetermined based on the first position of the leftmost boundary, thesecond position of the rightmost boundary, and/or the third position ofthe uppermost boundary. In some embodiments, according to a position ofthe heart in the thoracic cavity, the reconstruction center may bedetermined as a position in an upper left region of the thoracic contourcenter.

In some embodiments, the reconstruction center may be determined basedon the boundary position(s) of the thoracic contour image according tothe following equations:

$\begin{matrix}{{{CenX} = {{round}\left( {{Pos}_{l} + \left( \frac{{pos}_{r} - {pos}_{l}}{2} \right) + {OffsetX}} \right)}},} & {{Equation}(56)}\end{matrix}$ $\begin{matrix}{{{CenY} = {{round}\left( {{Pos}_{up} + \frac{FoV}{2} + {OffsetY}} \right)}},} & {{Equation}(57)}\end{matrix}$

where OffsetX is a factor used to adjust the distance of thereconstruction center to the left of the contour center (of the thoraciccontour); OffsetY is a factor used to adjust the distance of thereconstruction center upward from the contour center (of the thoraciccontour); Pos_(l) denotes the leftmost boundary position of the thoraciccontour image; Pos_(r) denotes the rightmost boundary position of thethoracic contour image; Pos_(up) denotes the uppermost position of thethoracic contour image.

In 21418, one or more target cardiac images may be reconstructed, in apreset FOV and at the reconstruction center.

In some embodiments, the processing device 140 (e.g., the imagereconstruction module 28400) may perform operation 21418.

Specifically, in some embodiments, the multi-phase reconstruction may beperformed according to the determined reconstruction center and thepreset reconstruction FOV. In some embodiments, the coronary artery mayhave a curved shape in an axial direction of the thoracic cavity, andaccordingly, position(s) of the coronary artery in the axial directionof the thoracic cavity may be inconstant. Therefore, in someembodiments, the preset reconstruction FOV may not be set too small. Insome embodiments, an exemplary reconstruction FOV may have a diameter of80 mm. The reconstruction FOV may be an FOV for multi-phasereconstruction.

According to the cardiac image reconstruction method(s), apparatus(es),computing device(s) and computer readable storage medium(s), a pluralityof preview images may be obtained; a thoracic contour image may beobtained according to the plurality of preview images and a maximumintensity projection algorithm; the leftmost boundary position, therightmost boundary position, and/or the uppermost boundary position maybe determined according to the thoracic contour image, and accordingly,the reconstruction center may be determined. A multi-phasereconstruction may be performed according to the reconstruction centerand the preset reconstruction FOV to obtain the cardiac image(s). Insome embodiments, the heart position may be determined first, and thenmulti-phase image reconstruction may be performed according to the heartposition. Therefore, the amount of data input for the reconstruction maybe reduced, the time of data input may be further reduced, and theoperation efficiency may be improved.

It should be understood that although the various operations in theflowcharts of FIGS. 18-21 are displayed successfully as indicated by thearrows, these operations are not necessarily performed in the orderindicated by the arrows. Except as explicitly stated herein, there is nostrict ordering of the execution of these operations, and theseoperations may be performed in other orders. Moreover, at least a partof the operations in FIGS. 18-21 may include a plurality ofsub-operations or a plurality of stages, these sub-operations or stagesare not necessarily performed at the same time, but may be executed atdifferent times. The execution order of the sub-operations or stages isalso not necessarily successful, but may be performed alternately oralternately with other operations or at least a portion ofsub-operations or stages of other operations.

FIG. 28 is a block diagram illustrating an exemplary processing devicefor cardiac image reconstruction according to some embodiments of thepresent disclosure.

As shown in FIG. 28 , a processing device 140 b is provided. Theprocessing device 140 b may include: a maximum intensity projectionmodule 28100, a boundary determination module 28200, a centerreconstruction module 28300, and an image reconstruction module 28400.

The maximum intensity projection module 28100 may be configured toobtain a plurality of preview images, and/or generate a thoracic contourimage according to the plurality of preview images and a maximumintensity projection algorithm.

More descriptions of the maximum intensity projection module 28100 maybe found elsewhere in the present disclosure (e.g., FIGS. 18, 19, and 21and descriptions thereof).

The boundary determination module 28200 may be configured to determineboundary position(s) of the thoracic contour image according to thethoracic contour image.

More descriptions of the boundary determination module 28200 may befound elsewhere in the present disclosure (e.g., FIGS. 18, 20, and 21and descriptions thereof).

The center reconstruction module 28300 may be configured to determine areconstruction center according to one or more boundary positions of thethoracic contour image.

More descriptions of the center reconstruction module 28300 may be foundelsewhere in the present disclosure (e.g., FIGS. 18 and 21 anddescriptions thereof).

The image reconstruction module 28400 may be configured to perform imagereconstruction (e.g., multi-phase reconstruction) according to thereconstruction center and the preset FOV to obtain cardiac image(s).

More descriptions of the image reconstruction module 28400 may be foundelsewhere in the present disclosure (e.g., FIGS. 18 and 21 anddescriptions thereof).

FIG. 29 is a block diagram illustrating an exemplary maximum intensityprojection module according to some embodiments of the presentdisclosure.

As shown in FIG. 29 , a maximum intensity projection module 28100 isprovided. The maximum intensity projection module 28100 may include afirst image segmenting unit 29110, an opening operation unit 29120, anda maximum intensity projection unit 29130.

The first image segmenting unit 29110 may be configured to obtain one ormore preview images, and perform segmentation of the preview image(s) toobtain a first set of images.

The opening operation unit 29120 may be configured to perform an openingoperation on the first set of images to obtain a second set of images.

The maximum intensity projection unit 29130 may be configured to performa maximum intensity projection on the first set of images and/or thesecond set of images respectively to obtain a thoracic contour image.

FIG. 30 is a block diagram illustrating an exemplary maximum intensityprojection unit according to some embodiments of the present disclosure.

As shown in FIG. 30 , a maximum intensity projection unit 29130 isprovided. The maximum intensity projection unit 29130 may include afirst maximum intensity projection sub-unit 30131, a second maximumintensity projection sub-unit 30132, and a difference determinationsub-unit 30133.

The first maximum intensity projection sub-unit 30131 may be configuredto perform a maximum intensity projection on the first set of images inthe axial direction of the thoracic cavity to obtain a maximum intensityprojection image of the first set of images.

The second maximum intensity projection sub-unit 30132 may be configuredto perform a maximum intensity projection on the second set of images inthe axial direction of the thoracic cavity to obtain a maximum intensityprojection image of the second set of images.

The difference determination sub-unit 30133 may be configured todetermine a difference between the maximum intensity projection image ofthe first set of images and the maximum intensity projection image ofthe second set of images to obtain a thoracic contour image.

FIG. 31 is a block diagram illustrating an exemplary boundarydetermination module according to some embodiments of the presentdisclosure.

As shown in FIG. 31 , a boundary determination module 28200 is provided.The boundary determination module 28200 may include a second imagesegmentation unit 31210, a left boundary determination unit 31220, aright boundary determination unit 31230, and an upper boundarydetermination unit 31240.

The second image segmentation unit 31210 may be configured to performone or more segmentation operations on the thoracic contour image toobtain a left thoracic contour image and a right thoracic contour image.

The left boundary determination unit 31220 may be configured todetermine the leftmost boundary position according to the left thoraciccontour image.

The right boundary determination unit 31230 may be configured todetermine the rightmost boundary position according to the rightthoracic contour image.

The upper boundary determination unit 31240 may be configured todetermine the uppermost boundary position according to the thoraciccontour image.

FIG. 32 is a block diagram illustrating an exemplary left boundarydetermination unit according to some embodiments of the presentdisclosure.

As shown in FIG. 32 , a left boundary determination unit 31220 isprovided. The left boundary determination unit 31220 may include a firstconnected domain determination sub-unit 32221 and a left boundarydetermination sub-unit 32222.

The first connected domain determination sub-unit 32221 may beconfigured to determine a maximum connected domain according to the leftthoracic contour image, and select elements in the largest connecteddomain of the left thoracic contour image.

The left boundary determination sub-unit 32222 may be configured toselect elements in an upper part of the right coronary in the largestconnected domain of the left thoracic contour image, and determine theleftmost boundary position.

FIG. 33 is a block diagram illustrating an exemplary right boundarydetermination unit according to some embodiments of the presentdisclosure.

As shown in FIG. 33 , a right boundary determination unit 31230 isprovided. The right boundary determination unit 31230 may include asecond connected domain determination sub-unit 33231 and a rightboundary determination sub-unit 33232.

The second connected domain determination sub-unit 33231 may beconfigured to determine a maximum connected domain according to theright thoracic contour image, and select elements within the largestconnected domain of the right thoracic contour image.

The right boundary determination sub-unit 33232 may be configured toselect elements in an upper part of the right coronary in the largestconnected domain of the right thoracic contour image, and determine therightmost boundary position.

FIG. 34 is a block diagram illustrating an exemplary upper boundarydetermination unit according to some embodiments of the presentdisclosure.

As shown in FIG. 34 , an upper boundary determination unit 31240 isprovided. The upper boundary determination unit 31240 may include acandidate region of interest determination sub-unit 34241, a thirdconnected domain determination sub-unit 34242, and an upper boundarydetermination sub-unit 34243.

The candidate region of interest determination sub-unit 34241 may beconfigured to determine a region to be analyzed in the thoracic contourimage according to the rightmost position of the left thoracic contourimage, the leftmost position of the right thoracic contour image, andthe lowest position of the second set of images.

The third connected domain determination sub-unit 34242 may beconfigured to determine a maximum connected domain according to theregion to be analyzed, and select elements within the largest connecteddomain of the thoracic contour image.

The upper boundary determination sub-unit 34243 may be configured todetermine the uppermost boundary position according to the elements inthe largest connected domain of the thoracic contour image.

For specific definitions of the card image reconstruction device, referto the above definition of the cardiac image reconstruction method, anddetails are not repeated herein. The various modules in the cardiacimage reconstruction apparatus can be implemented in whole or in part ofsoftware, hardware, and combinations thereof. The modules may beembedded in or independent of the processing device 140 in the computingdevice, or may be stored in the memory of the computing device in theform of software, so that the processing device 140 may call to performthe operations corresponding to the above modules.

FIG. 35 is a schematic diagram illustrating an exemplary computingdevice according to some embodiments of the present disclosure.

As shown in FIG. 35 , a computing device 3500 is provided. The computingdevice 3500 may be a terminal. The internal components of the computingdevice 3500 may be shown in FIG. 35 . The computing device 3500 mayinclude a processor 3510, a memory, a network interface 3550, a displayscreen 3560, and an input device 3570 connected by a system bus 3520.The processor 3510 of the computing device 3500 may provide computingand/or control capabilities. The memory of the computing device 3500 mayinclude a non-volatile storage medium 3530, an internal memory 3540. Thenon-volatile storage medium 3530 may store an operating system 3531 andcomputer program(s) 3532. The internal memory 3540 may provide anenvironment for operation of the operating system 3531 and the computerprogram(s) 3532 in the non-volatile storage medium 3530. The networkinterface 3550 of the computing device 3500 may communicate with anexternal terminal via a network connection. The computer program(s) 3532may be executed by the processor 3510 to implement a cardiac imagereconstruction process. The display screen 3560 of the computing device3500 may include a liquid crystal display or an electronic ink displayscreen, and the input device 3570 of the computing device 3500 mayinclude a touch layer covered on the display screen, or may include abutton, a trajectory ball or a touchpad provided on the casing of thecomputing device. It may also be an external keyboard, trackpad, ormouse, or the like.

It will be understood by those skilled in the art that the structureshown in FIG. 35 is only a block diagram of a part of the structurerelated to the present disclosure, and does not constitute a limitationon the computing device on which the present disclosure scheme isapplied. The computing device may include more or fewer components thanthose shown in the figures, or some components may be combined, or havedifferent component arrangements.

In some embodiments, a computer apparatus is provided comprising amemory and a processor having computer program(s) stored therein. Theprocessor may implement one or more of the following operations whenexecuting the computer program(s).

A plurality of preview images may be obtained as input, and a thoraciccontour image may be obtained according to the preview image(s) and amaximum intensity projection algorithm; the boundary position(s) of thethoracic contour image may be determined according to the thoraciccontour image; a reconstruction center may be determined according tothe boundary position(s) of the thoracic contour image; and multi-phasereconstruction may be performed according to the reconstruction centerand the preset reconstruction FOV to obtain the cardiac image(s).

In some embodiments, the processing device 140 may implement one or moreof the following operations when executing the computer program(s).

A plurality of preview images may be obtained as input; the previewimage(s) may be segmented to obtain a first set of images; an openingoperation may be performed on the first set of images to obtain a secondset of images; and a maximum intensity projection may be performed onthe first set of images and the second set of images, respectively, toobtain a thoracic contour image.

In some embodiments, the processing device 140 may implement one or moreof the following operations when executing the computer program(s).

The thoracic contour image may be segmented to obtain a left thoraciccontour image and a right thoracic contour image; the leftmost boundaryposition may be determined according to the left thoracic contour image;the rightmost boundary position may be determined according to the rightthoracic contour image; and the uppermost boundary position may bedetermined based on the thoracic contour image.

In some embodiments, the processing device 140 may implement one or moreof the following operations when executing the computer program(s).

A plurality of preview images may be obtained as input; the previewimage(s) may be segmented to obtain a first set of images; an openingoperation may be performed on the first set of images to obtain a secondset of images; a maximum intensity projection may be performed on thefirst set of images and the second set of images respectively to obtaina thoracic contour image; the thoracic contour image may be segmented toobtain a left thoracic contour image and a right thoracic contour image;the leftmost boundary position may be determined according to the leftthoracic contour image; the rightmost boundary position may bedetermined according to the right thoracic contour image; the uppermostboundary position may be determined according to the thoracic contourimage; a reconstruction center may be determined according to theboundary position(s) of the thoracic contour image; the multi-phasereconstruction may be performed according to the reconstruction centerand the preset reconstruction FOV to obtain the cardiac image(s).

In some embodiments, a non-transitory computer readable medium storinginstructions is provided. The instructions, when executed by theprocessing device, may cause the processing device to implement one ormore operations illustrated above.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer readable media having computer readableprogram code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C #, VB.NET, Python or the like, conventional procedural programming languages,such as the “C” programming language, Visual Basic, Fortran 2103, Perl,COBOL 2102, PHP, ABAP, dynamic programming languages such as Python,Ruby and Groovy, or other programming languages. The program code mayexecute entirely on the user's computer, partly on the user's computer,as a stand-alone software package, partly on the user's computer andpartly on a remote computer or entirely on the remote computer orserver. In the latter scenario, the remote computer may be connected tothe user's computer through any type of network, including a local areanetwork (LAN) or a wide area network (WAN), or the connection may bemade to an external computer (for example, through the Internet using anInternet Service Provider) or in a cloud computing environment oroffered as a service such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, for example, aninstallation on an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities or propertiesused to describe and claim certain embodiments of the application are tobe understood as being modified in some instances by the term “about,”“approximate,” or “substantially.” For example, “about,” “approximate,”or “substantially” may indicate ±20% variation of the value itdescribes, unless otherwise stated. Accordingly, in some embodiments,the numerical parameters set forth in the written description andattached claims are approximations that may vary depending upon thedesired properties sought to be obtained by a particular embodiment. Insome embodiments, the numerical parameters should be construed in lightof the number of reported significant digits and by applying ordinaryrounding techniques. Notwithstanding that the numerical ranges andparameters setting forth the broad scope of some embodiments of theapplication are approximations, the numerical values set forth in thespecific examples are reported as precisely as practicable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the descriptions, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

What is claimed is:
 1. A method implemented on at least one machine eachof which has at least one processor and at least one storage device forreconstructing one or more target cardiac images, the method comprising:obtaining a plurality of projection data corresponding to a plurality ofcardiac motion phases; determining a plurality of cardiac motionparameters corresponding to at least a portion of the plurality ofcardiac motion phases based on the plurality of projection data;determining a phase of interest based on the plurality of cardiac motionparameters; and reconstructing the one or more target cardiac images ofthe phase of interest.
 2. The method of claim 1, wherein the determininga plurality of cardiac motion parameters corresponding to at least aportion of the plurality of cardiac motion phases based on the pluralityof projection data includes: reconstructing a plurality of cardiacimages of the at least a portion of the plurality of cardiac motionphases based on the plurality of projection data; obtaining a pluralityof mean absolute differences (MADs) by determining an MAD between twocardiac images of each two adjacent cardiac motion phases in the atleast a portion of the plurality of cardiac motion phases; anddetermining the plurality of cardiac motion parameters corresponding tothe at least a portion of the plurality of cardiac motion phases basedon the plurality of mean absolute differences.
 3. The method of claim 2,further comprising: preprocessing the plurality of cardiac images,including: extracting one or more regions relating to cardiac motion bysegmenting the plurality of cardiac images; and designating the one ormore regions relating to cardiac motion as the plurality of cardiacimages.
 4. The method of claim 1, wherein the determining a plurality ofcardiac motion parameters corresponding to at least a portion of theplurality of cardiac motion phases based on the plurality of projectiondata includes: for each cardiac motion phase of the at least a portionof the plurality of cardiac motion phases, determining a first meanabsolute difference (MAD) between a first cardiac image of a firstcardiac motion phase that occurs before the each cardiac motion phaseand a cardiac image of the each cardiac motion phase; determining asecond mean absolute difference (MAD) between a second cardiac image ofa second cardiac motion phase that occurs after the each cardiac motionphase and the cardiac image of the each cardiac motion phase; anddesignating a sum of the first mean absolute difference (MAD) and thesecond mean absolute difference (MAD) as the cardiac motion parametercorresponding to the each cardiac motion phase.
 5. The method of claim4, wherein the first cardiac motion phase is adjacent to the eachcardiac motion phase, and the second cardiac motion phase is adjacent tothe each cardiac motion phase.
 6. The method of claim 1, wherein thedetermining a plurality of cardiac motion parameters corresponding to atleast a portion of the plurality of cardiac motion phases based on theplurality of projection data includes: for each cardiac motion phase ofthe at least a portion of the plurality of cardiac motion phases,determining a set of initial cardiac images of interest based on the oneor more cardiac images of the each cardiac motion phase, an averagecardiac rate, and a standard deviation of cardiac rates; extracting oneor more vascular images of interest of the at least a portion of theplurality of cardiac motion phases from the set of initial cardiacimages of interest; and determining the cardiac motion parametercorresponding to the each cardiac motion phase based on the one or morevascular images of interest.
 7. The method of claim 6, wherein thedetermining the cardiac motion parameter corresponding to the eachcardiac motion phase based on the one or more vascular images ofinterest includes: determining a first vascular center in each vascularimage of interest of the each cardiac motion phase; determining adisplacement of the first vascular center based on a first position ofthe first vascular center in the each vascular image of interest and asecond position of a second vascular center in a vascular image ofinterest of another cardiac motion phase adjacent to the each cardiacmotion phase; determining an interval between the each cardiac motionphase and the another cardiac motion phase adjacent to the each cardiacmotion phase; and determining, based on the displacement and theinterval, a motion rate of the first vascular center; and designatingthe motion rate of the first vascular center as the cardiac motionparameter.
 8. The method of claim 1, wherein the plurality of projectiondata are generated in a plurality of cardiac cycles, and the determininga phase of interest based on the plurality of cardiac motion parametersincludes: determining a mean phase based on the plurality of cardiacmotion parameters corresponding to the at least a portion of theplurality of cardiac motion phases; for each of the plurality of cardiaccycles, selecting one or more cardiac motion phases in a preset phaserange in the each cardiac cycle of the plurality of cardiac cycles, thepreset range including the mean phase; reconstructing one or morecardiac images of the one or more cardiac motion phases based on one ormore sub-sets of projection data of the plurality of projection datacorresponding to the one or more cardiac motion phases in the eachcardiac cycle; determining the phase of interest in the each cardiaccycle based on the one or more cardiac images of the each cardiac cycle.9. A method implemented on at least one machine each of which has atleast one processor and at least one storage device for reconstructingone or more target cardiac images, the method comprising: obtaining aplurality of projection data corresponding to a plurality of cardiacmotion phases; determining a phase of interest based on the plurality ofprojection data using one or more deep neural networks; andreconstructing the one or more target cardiac images of the phase ofinterest.
 10. The method of claim 9, wherein the determining a phase ofinterest based on the plurality of projection data using one or moredeep neural networks includes: extracting, based on the plurality ofprojection data, one or more images of a region of interest (ROI) usinga first deep nueral network of the one or more deep neural networks; andselecting, based on projection data associated with the one or moreimages of the ROI, the phase of interest using a second deep nueralnetwork of the one or more deep neural networks.
 11. The method of claim10, wherein the first deep nueral network is trained using sample imagesof the ROI and one of sample projection data and sample raw data, thesample images of the ROI being obtained by: reconstructing sample imagesbased on the sample projection data or the sample raw data; andextracting the sample images of the ROI from the reconstructed sampleimages.
 12. The method of claim 11, wherein the first deep nueralnetwork is trained by: inputting the sample projection data or thesample raw data into the first deep nueral network; outputting sampleprojection data associated with the sample images of the ROI using thefirst deep nueral network; and adjusting parameters of the first deepnueral network based on the sample projection data associated with thesample images of the ROI, and the sample images of the ROI.
 13. Themethod of claim 10, wherein the second deep nueral network is trainedusing sample projection data associated with sample images of the ROIcorresponding to the plurality of cardiac motion phases, the sampleprojection data associated with the sample images of the ROI beinggenerated by performing a forward projection on the sample images of theROI.
 14. The method of claim 13, wherein the second deep nueral networkis trained by: inputting the sample projection data associated withsample images of the ROI corresponding to the plurality of cardiacmotion phases into the second deep nueral network to obtain an output ofthe second deep nueral network; and adjusting parameters of the seconddeep nueral network based on the output of the second deep nueralnetwork, and labels associated with the phase of interest.
 15. A methodimplemented on at least one machine each of which has at least oneprocessor and at least one storage device for reconstructing one or moretarget cardiac images, the method comprising: obtaining a plurality ofprojection data; determining a reconstruction center based on at leastone preview image associated with at least a portion of the plurality ofprojection data; and reconstructing, according to the reconstructioncenter, the one or more target cardiac images based on at least aportion of the plurality of projection data.
 16. The method of claim 15,wherein the at least one preview image is reconstructed in an initialfield of view (FOV) based on the at least a portion of the plurality ofprojection data.
 17. The method of claim 16, wherein the one or moretarget cardiac images are reconstructed in a preset FOV smaller than theinitial FOV.
 18. The method of claim 15, wherein the determining areconstruction center based on at least one preview image associatedwith at least a portion of the plurality of projection data includes:obtaining a thoracic contour image by performing a maximum intensityprojection on the at least one preview image; and determining thereconstruction center based on the thoracic contour image.
 19. Themethod of claim 18, wherein the determining the reconstruction centerbased on the thoracic contour image includes: determining one or morepositions of a thoracic contour boundary in the thoracic contour image;and determining the reconstruction center based on the one or morepositions of the thoracic contour boundary.
 20. The method of claim 18,wherein the obtaining a thoracic contour image by performing a maximumintensity projection on the at least one preview image includes:obtaining a first set of images by generating a first intermediate imagethrough segmenting each preview image of the at least one preview image;obtaining a corresponding second set of images by generating a secondintermediate image through performing an opening operation on the firstintermediate image; and obtaining the thoracic contour image byperforming a maximum intensity projection on the first set of images andthe second set of images.